Top 10 Technical Operations Engineer Interview Questions

1. How do you approach troubleshooting a production outage?

When troubleshooting a production outage, I first verify the issue by checking monitoring dashboards and alerts to understand the scope and impact. For example, during a recent database connectivity issue, I immediately checked our Datadog metrics to confirm which services were affected. Next, I gather information from logs using tools like ELK or Splunk to identify patterns or error messages. I then form a hypothesis based on the evidence - in that database incident, I suspected network connectivity problems between our application servers and database cluster. To test this, I ran network diagnostics and confirmed packet loss on a specific subnet. I always follow a methodical approach rather than making random changes that could worsen the situation. Communication is crucial during outages, so I make sure to update stakeholders through our incident management channels, providing regular status updates and estimated resolution times. I also document all troubleshooting steps taken for post-incident review. Once I've identified the root cause, I implement a solution - in the database case, we rerouted traffic through a secondary network path while the network team addressed the underlying issue. After resolving the immediate problem, I conduct a thorough post-mortem to prevent similar issues in the future, which might include implementing additional monitoring or automated recovery procedures.

2. Explain how you would design a scalable infrastructure for a high-traffic web application.

I would design a scalable infrastructure starting with a multi-tier architecture that separates web, application, and database layers. For the web tier, I'd implement a content delivery network like Cloudflare or Akamai to cache static content and reduce load on origin servers. Behind this, I'd set up load balancers using something like AWS ELB or NGINX to distribute traffic across multiple web servers. These web servers would be deployed in an auto-scaling group that can dynamically adjust capacity based on traffic patterns - for instance, scaling up during business hours and down during off-hours. For the application tier, I'd use containerization with Docker and orchestrate with Kubernetes to ensure consistent deployments and efficient resource utilization. This allows for horizontal scaling where we can simply add more pods when load increases. The database tier would use a combination of read replicas for scaling read operations and sharding for write-heavy workloads - I've previously implemented this using AWS RDS with multiple read replicas across availability zones. For data that needs to be accessed frequently, I'd implement a caching layer using Redis or Memcached to reduce database load. To ensure reliability, I'd design the infrastructure to span multiple availability zones or even regions with automated failover capabilities. Monitoring would be comprehensive, using tools like Prometheus for metrics, Grafana for visualization, and automated alerts for any performance degradation. Finally, I'd implement CI/CD pipelines to enable frequent, small deployments that minimize risk and allow for continuous improvement of the infrastructure.

3. How do you manage configuration across multiple environments?

I manage configuration across multiple environments using a combination of infrastructure as code and configuration management tools. At my previous company, we used Terraform to define our infrastructure components consistently across development, staging, and production environments. This allowed us to version control our infrastructure definitions and ensure that all environments had the same components, just with different scaling parameters. For application configuration, we implemented a hierarchical approach using Ansible, where we maintained a base configuration that applied to all environments, with environment-specific overrides for values that needed to differ. We stored these configurations in Git repositories, which gave us change history and the ability to roll back if needed. For secrets management, we used HashiCorp Vault with different access policies for each environment, ensuring that production credentials were only accessible to authorized personnel and systems. We also implemented a promotion model where configuration changes would flow from development to staging to production, with automated testing at each stage to catch any issues early. To prevent configuration drift, we ran regular compliance checks using tools like AWS Config and custom scripts that would alert us if any environment deviated from the expected state. For runtime configuration changes, we used feature flags managed through a central service that allowed us to enable or disable features selectively in different environments. This approach gave us a good balance between consistency across environments and the flexibility to have environment-specific settings when necessary. The entire process was integrated with our CI/CD pipeline, so configuration changes went through the same review and testing process as code changes.

4. What monitoring and alerting strategies have you implemented in previous roles?

In my previous role at a fintech company, I implemented a comprehensive monitoring and alerting strategy that focused on both system health and user experience. We used Prometheus as our primary metrics collection system, gathering data from all infrastructure components including servers, containers, databases, and network devices. For visualization, we built custom Grafana dashboards that provided both high-level overviews for executives and detailed technical views for engineers. Our alerting philosophy was based on the concept of "symptoms, not causes" - we primarily alerted on user-impacting issues rather than every internal anomaly. For example, instead of alerting on high CPU usage, we alerted on increased API response times or error rates that actually affected customers. We implemented a tiered alerting system with different severity levels: P1 alerts would page the on-call engineer 24/7, while P3 alerts would create tickets for the team to address during business hours. To reduce alert fatigue, we implemented dynamic thresholds that adapted to normal usage patterns and seasonality using tools like Datadog's anomaly detection. For log monitoring, we used the ELK stack with custom parsers that could identify patterns indicating potential issues before they became critical failures. We also implemented synthetic monitoring with tools like Pingdom and New Relic to regularly test critical user journeys from different geographic locations. This helped us detect regional issues that might not be apparent from our internal monitoring. All of our monitoring systems fed into PagerDuty for alert management and escalation, with clear runbooks linked to each alert type. We regularly reviewed our alerting effectiveness in post-mortems, adjusting thresholds and adding new monitors based on incidents that weren't caught early enough.

5. How do you approach capacity planning for infrastructure resources?

I approach capacity planning as a data-driven process that combines historical analysis with future growth projections. At my previous company, I started by establishing baseline metrics for all critical resources - CPU, memory, storage, network bandwidth, and database IOPS. Using Prometheus and custom dashboards, I tracked these metrics over several months to understand normal usage patterns, including daily and weekly cycles, as well as seasonal variations like holiday traffic spikes for our e-commerce platform. I then analyzed growth trends, looking at month-over-month increases in resource utilization and correlating them with business metrics like user growth and transaction volume. This helped me develop growth models that could predict future resource needs. For example, I noticed our database IOPS grew at approximately 1.5x the rate of new user signups, which helped me forecast database capacity needs. I also worked closely with product management to understand upcoming features that might significantly impact resource requirements. When a new video processing feature was planned, we ran load tests in staging environments to measure the additional CPU and storage requirements per user, then factored that into our capacity model. I implemented automated scaling for resources that supported it, like our Kubernetes clusters and object storage, but for resources that required manual scaling like database instances, I established thresholds at 70% utilization that would trigger procurement processes. This gave us enough lead time to expand capacity before hitting critical levels. I also built in redundancy and headroom for unexpected spikes or failures - typically 30% extra capacity beyond our projected peak needs. This approach helped us avoid both over-provisioning (which would waste money) and under-provisioning (which would risk service degradation), while maintaining the flexibility to handle unexpected growth.

6. Describe your experience with automation and how it improved operational efficiency.

I've extensively used automation to transform manual, error-prone processes into reliable, consistent operations. At my previous company, we were spending approximately 20 hours per week on manual server patching across our 200+ server environment, with inconsistent results and occasional production issues from missed patches. I implemented an automated patching system using Ansible and AWS Systems Manager that reduced this to just 2 hours of oversight per week while improving our patch compliance from 85% to 99%. The system would automatically test patches in development environments, schedule production updates during appropriate maintenance windows, and roll back if monitoring detected any issues after application. Another significant automation project involved our deployment process, which previously required engineers to follow a 50-step checklist for each release. I built a CI/CD pipeline using Jenkins, Docker, and Kubernetes that automated the entire process from code commit to production deployment, reducing deployment time from 4 hours to 15 minutes and eliminating the human errors that had caused several outages. For incident response, I created automated runbooks in Rundeck that could execute common remediation steps like restarting services, clearing cache, or failing over to backup systems. This reduced our mean time to recovery for common issues from 45 minutes to under 10 minutes, even when handled by less experienced team members. I also automated our onboarding process for new team members, creating scripts that would automatically provision development environments, set up access permissions, and configure monitoring tools. This reduced onboarding time from two weeks to two days and ensured consistent environments across the team. Perhaps most importantly, I implemented infrastructure as code using Terraform, which allowed us to version control our entire infrastructure and deploy identical environments for development, testing, and production. This eliminated the "it works on my machine" problem and reduced environment-related bugs by approximately 70%.

7. How do you ensure security in your infrastructure and operations?

I ensure security through a defense-in-depth approach that addresses multiple layers of the technology stack. Starting with infrastructure, I implement strict network segmentation using security groups and VPCs in AWS to ensure that services can only communicate with other services that they legitimately need to interact with. For example, at my previous company, I redesigned our network architecture to place database servers in private subnets that were only accessible from application servers, not directly from the internet. For access control, I implement the principle of least privilege using IAM roles and policies, ensuring that both human users and services have only the permissions they absolutely need. I've used tools like AWS IAM Access Analyzer to audit permissions and identify overly permissive policies. I also implement secrets management using HashiCorp Vault, which provides encrypted storage for credentials and certificates with automatic rotation policies. For instance, we configured database credentials to rotate every 30 days without manual intervention. Vulnerability management is another critical aspect - I set up automated scanning of both infrastructure (using tools like Tenable) and application dependencies (using tools like Snyk) to identify and prioritize security issues. These scans run both on a schedule and as part of our CI/CD pipeline to catch vulnerabilities before they reach production. I also implement comprehensive logging and monitoring specifically for security events, using tools like AWS CloudTrail, VPC Flow Logs, and OSSEC to detect unusual patterns that might indicate a breach. These logs feed into our SIEM system where we've set up alerts for suspicious activities like unusual login locations or unexpected privilege escalations. For data protection, I implement encryption both at rest and in transit, using AWS KMS for managing encryption keys and enforcing TLS 1.2+ for all API communications. Regular security testing is essential, so I coordinate quarterly penetration tests with external security firms and run internal red team exercises to identify and address vulnerabilities before they can be exploited.

8. How do you handle database performance optimization?

Database performance optimization requires a systematic approach that combines monitoring, analysis, and targeted improvements. At my previous company, we were experiencing slow query performance on our PostgreSQL database that was affecting customer experience. I started by implementing detailed query performance monitoring using pg_stat_statements to identify the most resource-intensive queries. This revealed several queries that were performing full table scans on large tables. I analyzed the execution plans using EXPLAIN ANALYZE and found opportunities for optimization. For one particularly problematic query, I added appropriate indexes on the columns used in WHERE clauses, reducing query time from 2.3 seconds to 50 milliseconds. Beyond query-level optimizations, I also addressed database configuration. I tuned parameters like shared_buffers, work_mem, and effective_cache_size based on our server's available memory, which significantly improved overall throughput. For our read-heavy workload, I implemented a connection pooling solution using PgBouncer, which reduced connection overhead and allowed us to handle 3x more concurrent users with the same resources. I also implemented a data partitioning strategy for our largest tables, breaking them into smaller, more manageable chunks based on date ranges. This dramatically improved query performance on historical data and made maintenance operations like vacuuming much more efficient. For frequently accessed data that didn't change often, I implemented a caching layer using Redis, which reduced database load by approximately 40% during peak hours. Regular maintenance was also crucial - I set up automated jobs to analyze tables and update statistics, ensuring the query planner had accurate information to make good execution plan decisions. I also implemented a data archiving strategy that moved older, rarely accessed data to a separate database instance, keeping our primary database focused on current, frequently accessed data. Throughout this process, I maintained close communication with the development team, providing them with guidelines for writing efficient queries and reviewing new database-intensive features before they reached production.

9. What is your approach to disaster recovery planning?

My approach to disaster recovery planning starts with a business impact analysis to understand the criticality of different systems and establish appropriate Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs) for each. At my previous company, we categorized our systems into tiers: Tier 1 systems needed recovery within 1 hour with data loss under 5 minutes, while Tier 3 systems could tolerate 24+ hours of downtime. Based on these requirements, I designed appropriate backup and replication strategies. For our critical payment processing system, we implemented synchronous database replication across availability zones with automated failover capabilities, ensuring minimal data loss in case of a zone failure. For less critical systems, we used daily backups with point-in-time recovery options. I believe in documenting detailed recovery procedures for different disaster scenarios, from single service failures to complete region outages. These procedures were written as step-by-step runbooks that even team members unfamiliar with the systems could follow under pressure. For example, our database failover procedure included specific commands, expected outputs, and verification steps to ensure the process was completed correctly. Regular testing is absolutely essential for disaster recovery plans. We conducted quarterly tabletop exercises where we walked through recovery procedures for hypothetical scenarios, and twice-yearly live drills where we actually triggered failures in non-production environments to verify our recovery capabilities. During one such drill, we discovered that our database failover process was taking 15 minutes instead of the expected 5 minutes, allowing us to optimize the procedure before a real disaster struck. I also implemented automated recovery for common failure scenarios. For instance, we used AWS Auto Scaling Groups with health checks to automatically replace failed application servers, and Lambda functions that could detect and remediate specific infrastructure issues without human intervention. Communication plans are another critical component - I established clear protocols for who needed to be notified during different types of incidents, what communication channels would be used if primary systems were down, and how we would communicate with customers during extended outages.

10. How do you stay current with evolving technologies and best practices in technical operations?

I stay current with technology through a multi-faceted approach that combines hands-on experimentation, community engagement, and structured learning. I dedicate at least 5 hours each week to reading technical blogs and newsletters from companies like Netflix, Google, and Stripe that are known for their operational excellence. Their engineering blogs often detail solutions to problems similar to what we face, just at a different scale. I'm an active participant in several online communities, including the SRE subreddit and specific Slack channels for technologies we use like Kubernetes and Terraform. These communities provide real-time insights into emerging best practices and common pitfalls. For example, a discussion in the Kubernetes Slack channel alerted me to a memory leak issue in a specific version before we upgraded our production clusters. I also regularly attend virtual and in-person conferences like SREcon and AWS re:Invent, which provide both deep technical content and opportunities to network with peers facing similar challenges. After these events, I always share key learnings with my team through internal tech talks or documentation. Hands-on experimentation is crucial for truly understanding new technologies. I maintain a personal lab environment in AWS where I can test new tools and approaches before considering them for production use. Last year, I used this environment to evaluate several service mesh technologies, which led to our adoption of Istio for our microservices architecture. I also participate in open source projects related to our technology stack, which gives me insight into the roadmaps and design decisions behind the tools we rely on. For more structured learning, I complete at least two in-depth technical courses each year. Recently, I completed the Certified Kubernetes Administrator certification and a comprehensive course on site reliability engineering practices. I also organize a monthly "tech radar" meeting with my team where we collectively review new technologies and assess their potential value to our organization. This collaborative approach ensures we don't miss important developments and helps build consensus around technology adoption decisions.