Engineering Manager’s Questions

Technical Questions

1. How would you approach debugging a service that's experiencing intermittent latency spikes?

Great Response: "I'd take a structured approach starting with monitoring data. First, I'd check metrics around the time of the spikes to identify patterns - is it tied to certain times of day, traffic volumes, or deployment events? I'd look at system resources (CPU, memory, disk I/O) and application metrics together. If no obvious correlation appears, I'd implement distributed tracing if not already available to pinpoint where in the request flow the latency occurs. For persistent issues, I might set up conditional debug logging that triggers only during latency events. Throughout the process, I'd document findings and create a timeline of the investigation. Once I identify the root cause, I'd implement a fix, validate with tests, and enhance monitoring to catch similar issues earlier in the future."

Mediocre Response: "I would look at our monitoring dashboards to see what's happening during the spikes. I'd check CPU, memory, and database connection metrics. If nothing stands out, I'd look at recent code deployments that might have caused the issue. I might also restart the service to see if that helps while I continue investigating. I would probably add some logs to help debug the issue in the future."

Poor Response: "I would immediately add more resources to the service since latency usually means we're hitting resource limits. I'd scale up the instances and increase the CPU allocation. If that doesn't work, I'd check for any recent code changes and probably roll back to the previous version. I'd also ask developers if they've made any changes to how the service works that might explain the latency."

2. Explain your approach to capacity planning for a critical service.

Great Response: "I approach capacity planning as a data-driven, iterative process. I start by establishing clear SLOs for the service and identifying the key resources (compute, memory, storage, network) that constrain performance. I analyze historical usage patterns and growth trends, looking for both steady growth and seasonal variations. I create forecasting models that account for organic growth, planned product changes, and marketing events, then validate them against historical data. I build in a buffer of 30-50% depending on criticality and uncertainty of forecasts. Most importantly, I implement early warning systems that alert when we approach 70-80% of capacity and have documented scaling procedures ready. I revisit predictions quarterly against actual usage to refine our models."

Mediocre Response: "For capacity planning, I look at current usage and add a safety buffer, usually about 20%. I check how fast usage has been growing and extrapolate that into the future. I also talk to the product team to find out about upcoming features that might increase load. Based on all this, I estimate how much capacity we'll need for the next 6-12 months and plan accordingly. I make sure we have enough headroom to handle unexpected spikes."

Poor Response: "I would look at current maximum usage and double it to be safe. We can always add more resources if we need them since we're in the cloud. I'd make sure our auto-scaling is configured properly so the system can handle unexpected load. The most important thing is to have alerts set up so we know when we're running out of capacity and can quickly add more resources."

3. How do you determine appropriate SLOs (Service Level Objectives) for a new service?

Great Response: "Setting SLOs is about balancing user expectations with engineering reality. First, I'd identify the critical user journeys the service supports and what metrics truly matter to those users - is it availability, latency, throughput, or data freshness? For a new service, I'd research industry benchmarks and competitors' performance as reference points. I'd then collaborate with product managers to understand business requirements and engineering leads to understand technical constraints. Once we have initial targets, I'd implement detailed monitoring and establish a baseline during a soft launch or beta period. After collecting real-world data, I'd refine the SLOs, making sure they're measurable, achievable, and tied to actual user experience. I also establish an error budget policy that defines what actions we take when SLOs are at risk. Finally, I'd plan to review SLOs quarterly as the service matures."

Mediocre Response: "To set SLOs, I'd start by looking at what similar services are targeting. For availability, we typically want at least 99.9% uptime. For latency, I'd measure p50 and p95 response times during testing and set reasonable targets based on those results. I'd talk to the product team to understand what level of performance they need. Once the service is running, we might need to adjust the SLOs based on what we see in production."

Poor Response: "I'd set standard SLOs like 99.99% availability and sub-second response times, which are pretty typical goals for production services. We should aim high to ensure good user experience. If the service can't meet these standards, we can always adjust them later once we understand the limitations better. The most important thing is to have some targets to work toward."

4. Describe how you would design an effective alerting strategy.

Great Response: "An effective alerting strategy follows a simple principle: alerts should be actionable, relevant, and properly prioritized. I start by identifying what truly impacts users or business metrics and focus alerts there. I create a tiered approach: P0/P1 alerts require immediate action and page someone 24/7; P2 alerts can wait until business hours; P3 alerts are tracked but don't need immediate attention. For each alert, I define clear response procedures documenting investigation steps and potential remediation actions. I'm careful to reduce alert fatigue by eliminating redundant alerts, implementing proper thresholds with hysteresis to prevent flapping, and using time-based thresholds for transient issues. I correlate related symptoms to surface root causes rather than symptoms. Finally, I regularly review alert frequency and response times, adjusting thresholds as needed and automating responses for common issues."

Mediocre Response: "I would set up alerts on key metrics like CPU, memory, disk space, and error rates. Critical alerts should page someone, while less important ones can go to email or Slack. I'd make sure alerts have clear descriptions so engineers know what's happening. We should review alerts regularly to make sure they're still useful and not too noisy. It's important to have runbooks for common alerts so engineers know how to respond."

Poor Response: "I'd monitor everything we can and set up alerts when metrics go outside normal ranges. We need to know about any potential issues before they become problems. I'd use standard monitoring tools to track system health and set thresholds based on when performance starts to degrade. When something triggers an alert, whoever is on-call should investigate and fix the issue or escalate if needed."

5. What's your approach to implementing automation in an SRE context?

Great Response: "I view automation as an investment that needs to deliver clear returns in reliability, efficiency, or both. My approach starts with identifying repetitive, error-prone, or time-consuming tasks by analyzing on-call records and team feedback. Before automating, I document the manual process thoroughly and verify it works consistently. When building automation, I follow software engineering best practices: version control, testing, code review, and CI/CD. I'm careful to avoid over-automating too early - for new processes, I start with partial automation with human checkpoints until we understand edge cases. I design automation systems with observability in mind, including detailed logging and metrics to track successes, failures, and performance. Most importantly, I ensure automation is understandable to the team - well-documented, with clear failure modes and manual override capabilities. Finally, I measure and report on the impact: time saved, error reduction, and mean time to recovery improvements."

Mediocre Response: "I look for tasks that we do repeatedly and write scripts to automate them. This saves time and reduces human error. I make sure the scripts have good error handling and logging so we can troubleshoot when things go wrong. For critical automated processes, I add monitoring to alert us if they fail. I focus on automating deployment processes, scaling operations, and routine maintenance tasks since those are common and time-consuming."

Poor Response: "I try to automate as much as possible because manual work is inefficient. Once we identify a manual process, I write scripts to handle it automatically. This frees up engineer time for more important work. I use standard tools like Ansible, Terraform, or custom scripts depending on what needs to be done. Automation is a key part of scaling operations efficiently."

6. How do you approach postmortem analysis after an incident?

Great Response: "I view postmortems as learning opportunities rather than blame exercises. I follow a structured process starting with assembling key participants and a neutral facilitator. We create a detailed timeline using logs, metrics, and participant recollections, focusing on establishing facts before analysis. When analyzing root causes, I use techniques like the 5 Whys or Ishikawa diagrams to go beyond technical symptoms to systemic issues. I distinguish between the triggering event, contributing factors, and missed opportunities for earlier detection or prevention. For action items, I prioritize systemic improvements over band-aid fixes - things like enhancing monitoring, improving testing, addressing knowledge gaps, or changing processes. Each action item has a specific owner and deadline. Finally, I ensure we share lessons broadly across the organization and actually implement the action items, with regular follow-ups to verify completion. A good postmortem should help prevent entire classes of similar failures, not just the specific scenario that occurred."

Mediocre Response: "After an incident, I gather the involved team members to discuss what happened. We document the timeline of events, what went wrong, and how we fixed it. We try to identify the root cause and come up with action items to prevent similar issues in the future. I make sure someone is assigned to each action item with a deadline. We share the postmortem document with other teams who might benefit from the lessons learned."

Poor Response: "For postmortems, I document what broke and how we fixed it so we have a record for future reference. We identify what caused the issue and make sure we fix that specific problem. I assign action items to team members to implement fixes like adding more monitoring or updating our runbooks. The main goal is to make sure the exact same failure doesn't happen again."

7. How would you design a monitoring system for a distributed microservice architecture?

Great Response: "For distributed microservices, effective monitoring requires a multi-layered approach that combines infrastructure, application, and business metrics with distributed tracing. I'd start with the foundation: infrastructure metrics (CPU, memory, network) per service and host. Then I'd implement the RED pattern (Rate, Errors, Duration) for all service endpoints, plus custom application metrics for important business operations. I'd establish distributed tracing across services using technologies like OpenTelemetry to track request flows and identify bottlenecks. For observability, I'd ensure consistent structured logging with correlation IDs tied to traces. Beyond technical metrics, I'd implement user journey tracking to understand end-to-end experience. For visualization, I'd create purpose-built dashboards: operational ones for on-call engineers, service-specific ones for developers, and business dashboards for stakeholders. Finally, I'd implement anomaly detection that baselines normal behavior and alerts on deviations rather than static thresholds."

Mediocre Response: "I would use a combination of metrics, logs, and traces to monitor the microservices. Each service should export basic health metrics like request rate, error rate, and latency. We'd use a centralized logging system to collect and analyze logs from all services. For more complex issues, distributed tracing would help us follow requests across multiple services. I'd set up dashboards for each service showing its key metrics and create alerts for important thresholds."

Poor Response: "I would implement a centralized monitoring solution that tracks CPU, memory, and disk usage for all services. We'd monitor API endpoints to track success rates and response times. Each service would send logs to a central logging system where we can search for errors. We'd set up alerts when metrics exceed thresholds, like when error rates go above 1% or response times exceed certain limits."

8. Explain how you would implement a zero-downtime database migration.

Great Response: "Zero-downtime database migrations require careful planning and execution in several phases. First, I ensure the application code is backward and forward compatible, able to work with both old and new schema versions. I implement schema changes using non-blocking operations where possible (like adding nullable columns). For more complex changes, I use a multi-step approach: first add new structures without modifying existing ones, then deploy application code that can write to both old and new structures, backfill data from old to new structures, switch reads to use the new structures, and finally clean up old structures once they're no longer referenced. Throughout the process, I closely monitor database performance metrics, replication lag, and application errors. I always have a tested rollback plan for each step. Before executing in production, I would validate the entire migration process in a staging environment with production-like data volumes and traffic patterns. For critical systems, I might use a shadow environment to verify the new schema with production traffic without affecting users."

Mediocre Response: "I would use a blue-green deployment approach for the database migration. First, I'd set up a new database instance with the updated schema. Then I'd use a data migration tool to copy and transform the data from the old database to the new one. Once the initial data is copied, I'd set up ongoing replication to keep the new database in sync. When ready to switch over, I'd update the application configuration to point to the new database and deploy the updated application code. If there are problems, we can quickly switch back to the old database."

Poor Response: "I would schedule the migration during a maintenance window when traffic is lowest. I'd make a backup of the database first, then apply the schema changes. For large tables, I'd break down the migration into smaller chunks to reduce the impact. The application would need to be taken offline briefly during the final cutover, but we'd minimize this time as much as possible. After the migration, we'd thoroughly test the application to make sure everything works correctly before making it available to users again."

9. How would you approach scaling a service that's experiencing rapid growth?

Great Response: "Scaling for rapid growth requires both immediate tactical responses and strategic planning. My first step would be ensuring we have comprehensive metrics and dashboards to understand exactly what resources are bottlenecking - whether it's compute, memory, database connections, or external dependencies. In the short term, I'd implement horizontal scaling by adding more instances and enabling auto-scaling based on appropriate metrics. I'd look for quick optimizations like caching frequently accessed data, optimizing database queries, or adjusting timeouts and connection pools. For the medium term, I'd analyze traffic patterns to identify opportunities for efficiency improvements - things like batching requests, implementing circuit breakers for dependencies, or optimizing resource-intensive operations. Long-term, I'd evaluate architectural changes like implementing CQRS to separate read and write workloads, introducing message queues to handle traffic spikes, or sharding data. Throughout this process, I'd ensure we're not sacrificing observability or reliability for scale, and would continuously validate that our scaling approach is keeping pace with growth projections."

Mediocre Response: "I would first look at horizontal scaling by adding more instances of the service behind a load balancer. I'd implement auto-scaling based on CPU or request metrics to handle traffic spikes. Database scaling might require read replicas or sharding depending on the bottleneck. I'd also look for performance optimizations like adding caching layers and optimizing slow queries. We might need to consider moving some processing to asynchronous job queues if that's appropriate for the workload."

Poor Response: "The quickest approach would be to scale up the resources allocated to the service - more CPU, memory, and disk space. We could also add more instances behind the load balancer to handle increased traffic. If the database is the bottleneck, we might need a bigger database server. We should also optimize any slow code paths and add caching where possible to reduce load."

10. What strategies would you use to reduce Mean Time to Recovery (MTTR) for critical services?

Great Response: "Reducing MTTR requires improvements across the entire incident lifecycle. First, I'd focus on detection by implementing multi-layered monitoring that catches issues from various angles - user impact metrics, application performance, and infrastructure health. I'd use anomaly detection to identify issues before they become major outages. For diagnosis, I'd ensure we have comprehensive observability with correlated metrics, logs, and traces that help quickly pinpoint root causes. I'd create service maps showing dependencies to understand blast radius during incidents. For remediation, I'd implement automated recovery for common failure scenarios like restarting unhealthy services, failing over to standby systems, or shedding non-critical load. I'd maintain a library of runbooks with step-by-step recovery procedures for known failure modes. Beyond technical solutions, I'd optimize the incident response process with clear roles, communication channels, and escalation paths. After each incident, I'd analyze not just what broke but how we could have detected and resolved it faster, then implement those improvements. Finally, I'd conduct regular chaos engineering exercises and incident simulations to practice recovery procedures under controlled conditions."

Mediocre Response: "To reduce MTTR, I'd focus on improving both detection and resolution speed. For detection, we need good monitoring and alerting to quickly identify when something's wrong. I'd implement health checks and SLO monitoring for critical services. For faster resolution, I'd create detailed runbooks for common failure scenarios and automate recovery steps where possible. Having good dashboards that help troubleshoot common issues is important. We should also do regular drills to practice handling incidents so the team knows what to do when problems occur."

Poor Response: "The key to reducing MTTR is having good alerting so we know when something breaks, and then having enough staff available to respond quickly. I'd make sure we have 24/7 on-call coverage and clear escalation procedures. We should have backup systems ready to go when primary systems fail. After incidents, we need to fix the root cause quickly to prevent the same problem from happening again."

Behavioral/Cultural Fit Questions

11. Describe a time when you had to make a difficult trade-off between speed and reliability.

Great Response: "At my previous company, we were preparing for a major product launch with a fixed deadline when we discovered a performance issue affecting about 5% of user requests under specific conditions. The complete fix would require a significant refactoring that would take at least two weeks, jeopardizing the launch date. After analyzing the issue, I gathered the key stakeholders - product, engineering, and customer support - to discuss options. I presented data showing the limited impact scope, a mitigation plan using caching that could reduce the occurrence by 80%, and a proposed monitoring and alerting strategy to catch affected users. I outlined a detailed plan to implement the complete fix post-launch with clear milestones. We collectively decided to proceed with the mitigation approach for launch while transparently communicating the limitation to our customer success team. Post-launch, we prioritized the refactoring and completed it within three weeks. This approach balanced business needs with technical reality while ensuring we had proper observability and a clear remediation timeline."

Mediocre Response: "We had a deadline for a major feature release, but we discovered some performance issues in our final testing. We didn't have enough time to fix everything properly before the release date. I discussed the situation with my manager and the product team, and we decided to move forward with the release since the issues weren't critical. We added extra monitoring to watch for problems and planned to address the performance issues in the next sprint. The release went mostly fine, though we did have to deal with some customer complaints about slowness that we were able to resolve with targeted fixes."

Poor Response: "We were under pressure to deliver a new service by the end of the quarter. During testing, we found some reliability issues, but fixing them would take too much time. I decided we needed to meet the deadline, so we deployed with known issues but added extra monitoring. We had some outages after launch that the on-call team had to handle, but we gradually fixed the problems over the next few weeks. Meeting the deadline was essential for the business, so it was the right call even though it created some short-term reliability issues."

12. How do you handle situations where you disagree with your team's approach to solving a problem?

Great Response: "When I disagree with my team's approach, I first make sure I fully understand their perspective by asking clarifying questions and listening actively. Rather than focusing on my preferred solution, I articulate my concerns in terms of specific risks or limitations I see with the proposed approach. I use data and examples where possible rather than opinions. If the disagreement persists, I'll suggest running a quick experiment or proof-of-concept to test assumptions. Most importantly, I recognize that consensus is valuable but not always necessary - once a direction is chosen, even if it wasn't my preferred option, I commit fully to making it successful. I remember a situation where my team wanted to implement a custom load balancing solution rather than using our cloud provider's managed offering. I had concerns about long-term maintenance costs, but after discussion, we agreed to a time-boxed implementation with clear success criteria and a commitment to reevaluate after three months. Even though it wasn't initially my preference, I contributed actively to the design and implementation, and we ultimately created a solution that worked better for our specific needs than I had anticipated."

Mediocre Response: "When I disagree with my team, I explain my concerns and the reasons behind them. I try to back up my position with data or examples from past experience. I listen to their perspective as well to understand why they're suggesting a different approach. Usually, we can find a middle ground that addresses everyone's concerns. If the team decides to go in a direction I don't fully agree with, I support the decision and help implement it as best I can. It's important to present a unified front once decisions are made."

Poor Response: "I would voice my concerns in the team meeting and explain why I think my approach is better. If the team still wants to go another way, I defer to the majority decision or what my manager thinks is best. I'll document my concerns so there's a record if problems come up later. At the end of the day, we need to work together as a team, so I go along with whatever is decided even if I think it's not optimal."

13. Tell me about a time when you had to learn a new technology quickly to solve a problem.

Great Response: "Our team faced an issue where our logging system couldn't handle the volume during traffic spikes, causing us to lose critical data during incidents when we needed it most. We needed a solution quickly as we were approaching our busiest season. Although I had no prior experience with streaming data platforms, research suggested Kafka might be the right solution. I created a learning plan that combined practical application with theory - first working through basic tutorials to understand core concepts, then building a small proof-of-concept that integrated with our actual logging pipeline. I identified and connected with two engineers in different departments who had Kafka experience, and they provided invaluable guidance on cluster sizing and configuration best practices. Within two weeks, I had designed a solution and presented it to the team with performance benchmarks showing it could handle 20x our current peak volume. We implemented the solution incrementally, starting with non-critical services, and had full deployment before our traffic spike. The system performed flawlessly, and I documented everything I learned in an internal wiki that became a reference for other teams. This experience taught me that combining hands-on experimentation with expert consultation is the fastest way to productively learn new technology."

Mediocre Response: "We needed to implement a new monitoring solution because our existing tools weren't giving us enough visibility into our microservices. I hadn't used Prometheus before, but it seemed like the right tool for the job. I spent a few days reading the documentation and following tutorials to understand how it worked. I also watched some conference talks about Prometheus best practices. Once I felt comfortable enough, I set up a basic implementation for one of our services and gradually expanded it to cover our entire infrastructure. There was some trial and error involved, but within a couple of weeks, we had a working monitoring solution that gave us much better insights into our systems."

Poor Response: "We had a project that required using Docker containers, which I hadn't worked with before. I spent time reading online tutorials and the Docker documentation to understand the basics. I found some example configurations that were similar to what we needed and adapted them for our use case. It took longer than expected to get everything working correctly, but eventually, I figured it out. When I ran into specific problems, I searched for solutions on Stack Overflow. In the end, we got the containerized application deployed, though I'm still learning about best practices for container orchestration."

14. How do you balance fixing technical debt versus delivering new features?

Great Response: "I approach technical debt strategically rather than as an all-or-nothing proposition. First, I categorize debt by impact - does it affect reliability, developer productivity, or scalability? Then I quantify the ongoing cost of living with each debt item in measurable terms like increased incident frequency, slower development velocity, or rising infrastructure costs. With this data, I create a balanced roadmap that addresses high-impact debt alongside new feature work. I've found success using a budget-based approach, allocating roughly 20-30% of team capacity to debt reduction, adjusted based on current system health. For crucial debt that can't fit in this allocation, I build a business case showing the ROI of dedicated investment, demonstrating how much faster we could deliver future features after the improvements. I also look for opportunities to address debt incrementally alongside feature work when they touch the same components. Most importantly, I foster a culture where we discuss technical debt openly with product stakeholders, making it visible through metrics like time spent on maintenance or frequency of recurring issues, so it becomes a shared priority rather than an engineering-only concern."

Mediocre Response: "I try to maintain a balance by allocating some time in each sprint for technical debt work alongside new features. Usually, we'll dedicate about 20% of our capacity to addressing technical debt. I prioritize debt that's causing the most immediate pain, like issues that slow down development or cause production problems. When planning new features, I push for doing things the right way instead of taking shortcuts that would create more debt. For larger technical debt items, I work with product managers to schedule dedicated sprints where we focus primarily on improvement work, explaining how this investment will help us deliver features faster in the future."

Poor Response: "I focus on meeting our delivery commitments first, since that's what the business cares most about. When we have time between projects or during slower periods, we can tackle technical debt. If technical debt is causing actual problems in production, then I'll prioritize fixing it. Otherwise, I keep a backlog of technical improvement tasks that we can work on when we have bandwidth. Sometimes you have to push back on feature requests to create space for addressing critical technical debt."

15. Describe how you onboard new team members and help them become productive quickly.

Great Response: "My approach to onboarding is systematic but personalized, recognizing that getting new team members productive quickly benefits both the individual and the team. Before they start, I prepare a structured 30-60-90 day plan with clear milestones, and ensure their development environment and access are ready day one. I pair them with a dedicated mentor who meets with them daily for the first two weeks, then twice weekly afterward. For technical onboarding, I maintain a curated path through our codebase and architecture - starting with a simplified mental model and adding complexity gradually through a series of small, well-defined tasks that build confidence. These tasks progress from read-only (like adding logging or tests) to more complex changes. I schedule short sessions with key collaborators from other teams to build cross-functional relationships early. Weekly check-ins help identify and remove blockers quickly. I also customize the approach based on their learning style and prior experience - some prefer self-guided exploration with documentation, while others learn better through pair programming. The goal isn't just technical proficiency but also cultural integration and building relationships that make them effective in our specific environment."

Mediocre Response: "For new team members, I provide them with documentation about our systems and processes. I assign them a buddy who can answer their questions and help them navigate the team. I give them simple tasks to start with so they can get familiar with our codebase and deployment processes without feeling overwhelmed. We have regular check-ins to see how they're doing and address any concerns. I make sure they're introduced to key people they'll need to work with. As they become more comfortable, I gradually assign more complex tasks. I also encourage them to ask questions and provide feedback on our documentation and processes."

Poor Response: "I start new team members with our documentation and codebase overview. I assign them a small ticket to work on in their first week so they can go through our entire workflow from development to deployment. I make myself available to answer questions when they get stuck. We have team meetings where they can learn about what everyone is working on. I believe in learning by doing, so I get them involved in real work as quickly as possible. After a few weeks, they should be up to speed and contributing to the team."

16. How do you handle a situation where you're asked to deliver on an unrealistic timeline?

Great Response: "When faced with an unrealistic timeline, I first seek to understand the underlying business drivers and constraints rather than immediately pushing back. I schedule a conversation with stakeholders to clarify the true needs - sometimes what seems like a fixed deadline is actually flexible when we discuss priorities. Once I understand the context, I prepare a data-driven assessment showing what can realistically be delivered within the requested timeline, what would need to be deprioritized, and what risks would be introduced. I present options rather than problems - for example, an MVP approach that delivers core functionality by the deadline with a phased rollout of remaining features, or alternative technical approaches that might be faster but have different trade-offs. If the timeline truly cannot be met without unacceptable risk to reliability or quality, I clearly articulate these risks in business terms, like potential customer impact or future slowdowns from accumulated technical debt. Throughout this process, I maintain a collaborative tone focused on finding the best solution for the business rather than simply saying 'no.' In my experience, this approach usually leads to a compromise that addresses the most critical business needs while maintaining technical standards."

Mediocre Response: "I would analyze what can realistically be accomplished in the given timeframe and prepare an honest assessment. Then I'd meet with the stakeholders to discuss the situation, explaining what can be delivered by their deadline and what would have to be cut or simplified. I'd propose alternatives like phasing the delivery or reducing scope while still meeting the core requirements. If they insist on the original scope and timeline, I'd document the risks involved and make sure everyone understands the potential impact on quality or reliability. Sometimes we need to work extra hours to meet critical deadlines, but I try to avoid making that the regular solution."

Poor Response: "I would explain to management that the timeline isn't feasible and show them how long similar projects have taken in the past. I'd ask for more time or additional resources to meet the deadline. If they still insist on the original timeline, I'd do my best to deliver what I can by the deadline, focusing on the most important features first. I would make it clear that quality might suffer if we rush, and we might have to go back and fix things later. Sometimes you just have to push back on unrealistic expectations."

17. Tell me about a time you improved a process that enhanced your team's effectiveness.

Great Response: "I noticed our incident response process was causing delays and inconsistency in how we handled production issues. Response times varied widely, and we often had confusion about who was doing what during incidents. I began by collecting data on our last 15 incidents, measuring time to detection, time to mitigation, and time to resolution, along with qualitative feedback from team members involved. The data showed we were spending an average of 30 minutes just on coordination at the start of each incident. I proposed a new structured approach with clearly defined roles (incident commander, communicator, and technical lead), standardized severity levels with response expectations, and templated status updates. I created a playbook and built a simple chatbot that would automatically create the right communication channels and notify the appropriate people based on incident type. I ran a workshop to train the team and scheduled regular drills to practice the process. Within three months, our average time to mitigation dropped by 47%, and post-incident surveys showed team members felt much clearer about their responsibilities. What I'm most proud of is that several other teams adopted our approach after seeing its effectiveness, creating a more consistent incident response culture across the organization."

Mediocre Response: "Our deployment process was taking too long and causing a lot of frustration. Deployments often failed and had to be rerun, sometimes taking an entire day. I spent time analyzing the process and identified several points of failure. I automated many of the manual steps using scripts and improved our test suite to catch issues earlier. I also created better documentation so everyone understood the correct procedures. After implementing these changes, our deployments became much more reliable and typically finished in under an hour instead of taking all day. The team was much happier with the new process, and we could deploy more frequently, which made our product team happy too."

Poor Response: "I noticed our team was spending a lot of time in meetings that weren't very productive. I suggested we reduce the frequency of our status meetings from daily to twice a week and use a shared document to track progress instead. I created a template for the document that everyone could update with their status. This saved us about 30 minutes per day that we could use for actual work. People appreciated having fewer interruptions and more focused time. We still held the meetings twice a week to discuss any issues that came up."

18. How do you balance operational duties with project work?

Great Response: "Balancing operational work and project work requires both structural approaches and cultural alignment. I start with data - tracking what percentage of time is actually spent on operational tasks versus project work over several weeks to establish a baseline. This helps set realistic expectations about capacity. I implement a rotation system where team members alternate between being the primary on-call responder and focusing on project work, with clearly defined handoff procedures. For project planning, I build in a 'focus factor' that accounts for operational interruptions - typically planning for 60-70% project capacity depending on our current operational load. I've found that time-boxing operational work is effective - for example, dedicating mornings to operational tasks and escalated issues, then protecting afternoons for focused project work. Most importantly, I work with product and engineering leadership to establish shared understanding of SRE priorities, so when operational incidents force us to adjust project timelines, it's viewed as a necessary trade-off rather than a failure to deliver. I also regularly analyze patterns in operational work to identify opportunities for automation or process improvements that can gradually reduce the operational burden, creating a virtuous cycle where more time can be dedicated to strategic projects."

Mediocre Response: "I try to allocate specific time blocks for operational duties and project work. For example, I might dedicate mornings to handling operational tasks and alerts, and then focus on project work in the afternoons. I make sure our team has a clear on-call schedule so everyone knows when they need to prioritize operational issues. When planning projects, I account for the fact that operational duties will take some of our time, so I don't overcommit. I also try to automate repetitive operational tasks when possible to free up more time for project work. If there's a major incident, I understand that project work will need to be delayed, and I communicate this to stakeholders."

Poor Response: "I handle operational issues as they come up since they're usually more urgent than project work. When things are quiet, I can focus on project tasks. I keep a to-do list of project work and try to make progress on it between operational tasks. Sometimes you just have to work extra hours to keep up with both responsibilities. I prioritize based on what managers and stakeholders are asking for most urgently at any given time."

19. Describe a time when you had to push back on a decision that would negatively impact system reliability.

Great Response: "Our product team was planning to launch a major new feature that involved significant changes to our database schema and API layer. The launch date had been publicly announced, creating substantial pressure to meet the deadline. During the design review, I identified that the proposed implementation would double our database write load, but the team hadn't planned for capacity upgrades or tested the system under this increased load. Rather than simply saying 'no' to the launch, I prepared a detailed analysis showing current database utilization trends, the projected impact of the new feature, and historical examples of performance degradation when we'd approached similar capacity limits. I created a small proof-of-concept that simulated the increased load, demonstrating specific failure modes that would affect all users, not just those using the new feature. I then worked with the database team to develop three alternative approaches: a phased rollout that would allow us to monitor impact incrementally, schema optimizations that would reduce the write load, and a database scaling plan. I presented these options to stakeholders, framing it as a risk management decision rather than a binary yes/no. The product team ultimately chose to delay the launch by two weeks to implement the optimizations and scaling plan. The feature launched successfully with no reliability impact, and product leaders later thanked me for preventing what could have been a major outage."

Mediocre Response: "The marketing team wanted to launch a promotional campaign that would direct a large volume of traffic to our website, but they only gave us a week's notice. Based on their projections, we would exceed our current capacity by at least 50%. I explained to them that we needed more time to scale our infrastructure and test it under load. I showed them data from our load testing and previous traffic spikes where we'd experienced issues. After some discussion, they agreed to postpone the campaign by two weeks, which gave us enough time to properly prepare. We increased our server capacity, configured auto-scaling, and ran load tests to verify everything would handle the expected traffic. The campaign was ultimately successful with no performance issues."

Poor Response: "Our product manager wanted to skip load testing for a new feature to meet a deadline. I told them this was risky based on my experience with similar features. I explained that if the feature caused performance problems, it would affect the entire platform and potentially cause an outage. I suggested we at least do some basic load testing before release. They still wanted to proceed with the original plan, so I escalated to my manager who supported my position. We ended up doing abbreviated testing that found some issues we were able to fix before release. The product manager wasn't happy about the delay, but it prevented potential problems."