Preparing for Software Engineer, Product role at Meta

For Instagram's feed service at that scale, I'd implement a distributed architecture with multiple layers. The core would be a feed aggregation service using a fan-out-on-write approach for active users and fan-out-on-load for less active ones. This hybrid approach optimizes storage while maintaining performance for high-priority users.

For storage, I'd use a combination of Redis for hot data and Cassandra for persistent storage. The Redis layer would cache approximately 1000 most recent posts per user, with a TTL of 24-48 hours. Cassandra would store the complete feed data in a denormalized format optimized for read performance. I'd shard the data by user_id to distribute the load across multiple nodes.

To handle the massive read load, I'd implement a CDN layer for media content and edge caching for feed data with a typical cache hit rate target of 95%. The feed generation service would run on multiple regions using AWS or similar cloud infrastructure, with load balancing to direct users to the nearest datacenter. Based on previous experience, this architecture could handle 100k+ requests per second per region with sub-100ms latency.

For Meta's authentication system, I'd implement a multi-level caching strategy using Redis clusters. The first level would cache user session tokens with a short TTL (15-30 minutes) for active sessions, while the second level would maintain longer-lived refresh tokens (7-14 days). This approach balances security with user experience.

The main trade-offs involve consistency versus availability. I'd choose eventual consistency for non-critical user data but strong consistency for security-related information. To handle cache invalidation, I'd implement a pub/sub system using Redis or Kafka to propagate updates across all regions. The system would use write-through caching for critical updates and write-behind for non-critical data.

One key consideration is cache warming after failures. I'd implement a predictive cache warming system based on user activity patterns, preloading about 20% of most-accessed data. From past implementations, this typically reduces cold start times by 70-80% while keeping memory usage manageable.

In my previous role, I led an initiative to optimize a payment processing system that was experiencing significant latency issues during peak loads. The system was processing 10,000 transactions per minute but struggling with response times exceeding 2 seconds. Through systematic analysis, I identified that database queries were the primary bottleneck.

I implemented several optimizations: first, introducing a Redis caching layer for frequently accessed merchant data, reducing database load by 60%. Then, I rewrote key SQL queries to use proper indexing and materialized views, bringing query execution time down from 500ms to 50ms. Finally, I implemented database connection pooling and query parallelization.

The results were significant: average response time dropped to 200ms, system throughput increased to 25,000 transactions per minute, and database load decreased by 70%. This improvement allowed us to handle Black Friday traffic without adding hardware resources, saving approximately $50,000 in infrastructure costs.

For a high-scale notification system, I'd design an event-driven architecture using Apache Kafka as the backbone for message streaming. The system would be divided into three main components: event ingestion, processing, and delivery. The ingestion layer would handle incoming events through a REST API, validating and normalizing them before publishing to Kafka topics.

The processing layer would use Kafka Streams for real-time event processing, implementing fan-out distribution and notification aggregation. I'd use Redis for maintaining user connection states and notification preferences. For delivery, I'd implement multiple channels (WebSocket for real-time web, FCM for mobile, SMTP for email) with separate workers for each channel type.

To handle back-pressure, I'd implement rate limiting at both user and system levels, with configurable thresholds. Based on similar systems I've built, this architecture could handle 100,000+ notifications per second with sub-second delivery times for real-time notifications.

For maintaining data consistency across microservices, I'd implement a combination of event-driven architecture and saga pattern. The primary backbone would be an event bus using Kafka, where each service publishes domain events that other services can consume. This ensures loose coupling while maintaining eventual consistency.

For transactions that span multiple services, I'd implement the saga pattern with compensating transactions for rollbacks. Each microservice would maintain its own database, and we'd use the outbox pattern to ensure reliable event publishing. This approach prevents distributed transaction issues while ensuring data consistency.

To handle eventual consistency challenges, I'd implement version vectors for conflict resolution and use CDC (Change Data Capture) to track and reconcile data changes across services. From experience, this approach has helped achieve 99.99% data consistency while maintaining system availability above 99.9%.

In my previous role, I designed a payment processing API that initially handled basic transactions but needed to evolve to support new payment methods and regulatory requirements. We implemented URI-based versioning (e.g., /api/v1/, /api/v2/) which allowed us to maintain multiple versions simultaneously during migration periods. This approach provided explicit clarity for clients and simplified our API gateway routing.

We established a strict backwards compatibility policy where v1 endpoints remained functional for 12 months after v2 release. To manage this, we implemented a facade pattern where new versions could introduce new functionality while maintaining existing behavior. We used feature flags to gradually roll out changes and monitored version usage through custom metrics to inform deprecation timelines.

For documentation, we maintained separate OpenAPI specs for each version and implemented automated testing to ensure backwards compatibility wasn't broken. This approach allowed us to successfully migrate 50+ enterprise clients from v1 to v2 with zero downtime and minimal support issues.

The primary considerations I focus on are bounded contexts, data ownership, and interface design. First, I analyze business domains to identify natural service boundaries - each microservice should own its specific domain and data. For example, in an e-commerce system, order management and inventory management would be separate services with clear responsibilities.

Transaction management becomes crucial - I typically implement saga patterns for operations spanning multiple services, with compensating transactions for rollback scenarios. We must also carefully consider service discovery, inter-service communication patterns (synchronous vs asynchronous), and implementing circuit breakers for resilience.

Infrastructure considerations are equally important. I focus on implementing robust monitoring, distributed tracing (using tools like Jaeger), and centralized logging. Authentication and authorization need to be handled consistently across services, often using JWT tokens and an API gateway. Finally, I ensure each service has independent CI/CD pipelines and can be deployed autonomously.

For large-scale projects, I advocate for an API-first approach using OpenAPI (formerly Swagger) specifications as the single source of truth. This allows us to generate both client SDKs and server stubs automatically, ensuring documentation stays in sync with implementation. I typically set up automated workflows where OpenAPI spec changes must pass validation before merging to main.

For developer experience, I recommend combining OpenAPI with tools like Redoc or Swagger UI for interactive documentation. We supplement this with practical examples, SDK tutorials, and postman collections. For internal teams, we maintain additional documentation covering architectural decisions, deployment procedures, and troubleshooting guides in a wiki system like Confluence.

I also emphasize the importance of automated testing of documentation examples. We implement doc tests that validate all example requests/responses, ensuring they remain valid as the API evolves. This has significantly reduced support tickets and improved developer onboarding time.

My process starts with gathering detailed requirements through collaboration with product managers and stakeholders. I create a design document outlining the endpoint's purpose, request/response schemas, error scenarios, and expected performance characteristics. This document goes through peer review to catch potential issues early.

Next, I design the endpoint following REST principles, ensuring it fits our existing API patterns. I consider pagination for list endpoints, appropriate HTTP methods, status codes, and error handling. I update our OpenAPI specification first, which serves as a contract with consumers. For complex endpoints, I create sequence diagrams to illustrate interaction flows.

Implementation begins with writing integration tests based on the OpenAPI spec. I follow TDD principles, implementing the minimal code needed to make tests pass. I pay special attention to input validation, error handling, and logging. Before deployment, I ensure monitoring and alerts are configured, and perform load testing to verify performance under expected traffic patterns.

I implement a multi-tiered rate limiting strategy based on user categories and endpoint sensitivity. For example, free users might be limited to 100 requests per hour, while enterprise clients get 10,000. I typically use Redis to track request counts with sliding window counters, which provide more accurate rate limiting than fixed windows.

The implementation includes both rate limiting (max requests per time window) and throttling (requests per second) to prevent abuse and ensure service stability. Headers like X-RateLimit-Remaining provide clients visibility into their quota. For enterprise clients, we implement burst handling using token bucket algorithms, allowing them to temporarily exceed normal rates for legitimate spike scenarios.

We also maintain different rate limits for read vs write operations, and implement circuit breakers for critical service protection. All rate limiting decisions are logged and monitored, with alerts for repeated threshold violations. This helps us identify abuse patterns and adjust limits based on actual usage patterns.

My approach would start with rapid triage using our monitoring dashboards to identify the scope and impact. I'd immediately check key metrics like response times, error rates, CPU/memory usage, and database query performance across our services. This helps isolate whether it's a system-wide issue or specific to certain components.

For immediate diagnosis, I'd analyze recent deployments or configuration changes that could have triggered the degradation. I'd use distributed tracing tools like Jaeger or Zipkin to identify bottlenecks in request flows, and examine logs for any correlation with the timing of the performance drop. If necessary, I'd engage our on-call rotation for additional support.

Once the immediate cause is identified, I'd implement a short-term fix if needed (like rolling back a deployment or adjusting resource allocation), then conduct a thorough root cause analysis. This would involve analyzing metrics history, reviewing code changes, and possibly reproducing the issue in a staging environment. Finally, I'd document the incident and implement preventive measures like additional monitoring alerts or performance testing requirements.

For a critical backend service, I implement a comprehensive monitoring strategy across multiple layers. At the infrastructure level, I track CPU usage, memory consumption, disk I/O, and network metrics. For application performance, I monitor request latency (p50, p90, p99 percentiles), throughput, error rates, and queue depths. Database monitoring includes query performance, connection pool stats, and cache hit rates.

I believe in the USE method (Utilization, Saturation, Errors) combined with the RED method (Rate, Errors, Duration) for service monitoring. I set up alerting thresholds based on historical patterns and business SLAs, using tools like Prometheus for metrics collection and Grafana for visualization. For distributed systems, I implement distributed tracing using tools like OpenTelemetry to track requests across service boundaries.

Additionally, I maintain business-level metrics that matter to stakeholders, such as successful transactions per second or active user sessions. These metrics help bridge the gap between technical monitoring and business impact. All metrics are stored with appropriate retention policies and granularity levels to balance storage costs with troubleshooting needs.

In a recent project, I faced significant performance issues with our user activity tracking system that was processing millions of events daily. The database queries were taking increasingly longer to execute, affecting our real-time analytics dashboard. My methodology followed a structured approach to optimization.

First, I used database monitoring tools to identify the worst-performing queries through analysis of slow query logs. I found several queries lacking proper indexes and joining large tables inefficiently. I created execution plans using EXPLAIN ANALYZE to understand query paths and identify bottlenecks. This revealed that our most resource-intensive queries were doing full table scans on the events table.

I implemented several optimizations: created composite indexes based on common query patterns, denormalized certain frequently accessed data, and implemented partitioning by date range for the events table. I also introduced a caching layer using Redis for frequently accessed aggregate data. The results were significant: our main dashboard queries went from 2-3 seconds to under 200ms, and overall database load decreased by 60%.

In distributed systems, I implement a centralized logging strategy that ensures consistency and traceability across all services. Each log entry includes essential context: timestamp, service name, trace ID, span ID, severity level, and structured data in JSON format. I use correlation IDs to track requests across different services, making it possible to follow the entire request flow.

For collection and aggregation, I typically set up an ELK (Elasticsearch, Logstash, Kibana) stack or use cloud-native solutions like AWS CloudWatch. I implement log rotation and retention policies to manage storage costs while maintaining necessary historical data. Critical events are logged with additional context, and sensitive information is properly masked following security best practices.

I also emphasize the importance of log levels - using DEBUG for detailed troubleshooting, INFO for normal operations, WARN for potential issues, and ERROR for actual failures. This helps in filtering and alerting while keeping the signal-to-noise ratio manageable. For high-throughput services, I implement sampling strategies to reduce logging overhead while maintaining visibility into system behavior.

For performance profiling, I use a combination of tools depending on the specific aspect being analyzed. For application-level profiling, I use tools like async-profiler or YourKit for JVM applications, which provide detailed insights into CPU usage, memory allocation, and thread behavior. These tools help identify hot methods, memory leaks, and threading issues.

For system-level profiling, I rely on tools like perf, ftrace, or DTrace to understand kernel-level interactions and I/O patterns. Load testing is performed using tools like k6 or JMeter to simulate real-world usage patterns and identify bottlenecks under load. For database profiling, I use built-in tools like EXPLAIN ANALYZE and pg_stat_statements for PostgreSQL to analyze query performance.

I also implement continuous profiling in production using lightweight tools like pprof or async-profiler in sampling mode. This helps catch performance regressions early and provides historical data for comparison. All profiling data is collected and analyzed systematically, with careful attention to overhead and impact on production systems.

In a microservices architecture, I implement a comprehensive testing strategy that spans multiple layers. At the individual service level, I start with thorough unit testing of business logic and domain models, typically achieving 80%+ coverage. I use tools like Jest or JUnit depending on the tech stack, and implement extensive mocking of external dependencies.

For service-to-service interactions, I implement contract testing using tools like Pact or Spring Cloud Contract. This ensures that service interfaces remain compatible even as teams deploy independently. I've found contract testing particularly valuable in preventing integration issues in distributed systems.

For end-to-end testing, I take a pragmatic approach by identifying critical user journeys and implementing automated tests for these paths using tools like Cypress or Selenium. I also emphasize observability by implementing detailed logging, metrics, and distributed tracing using tools like OpenTelemetry, which helps debug issues in production.

My strategy for maintaining test coverage in growing codebases focuses on both process and culture. First, I establish automated coverage reporting in the CI/CD pipeline using tools like SonarQube or Codecov, setting minimum coverage thresholds that must be met for merge approval. This prevents coverage degradation over time.

I emphasize writing tests alongside new feature development rather than treating them as an afterthought. For legacy code, I follow the boy scout rule - leave the code better than you found it by adding tests when modifying existing functionality. I've successfully used this approach to gradually improve coverage in large systems from 40% to over 85%.

I also implement regular test maintenance sprints where we review and update our test suite, removing redundant tests, improving test performance, and ensuring our testing strategy evolves with our architecture. This includes periodic reviews of our testing pyramid distribution to maintain a healthy balance between unit, integration, and end-to-end tests.

For testing asynchronous operations and message queues, I implement a multi-layered approach. At the unit test level, I use mocking frameworks to simulate async behaviors and verify correct message handling. When testing with frameworks like Node.js or Spring, I leverage built-in testing utilities for async operations, ensuring proper error handling and timeout scenarios are covered.

For message queue testing, I typically set up isolated test environments with lightweight queue implementations like RabbitMQ or Redis. I've developed test helpers that allow us to publish messages and verify their processing, including error cases and retry mechanisms. This includes testing message ordering, dead letter queues, and concurrent message processing.

Integration tests for async systems utilize test doubles and time manipulation to verify end-to-end behavior without requiring long test execution times. I've successfully implemented this using tools like Testcontainers for spinning up isolated queue instances, combined with careful test timing control to ensure reliable test execution.

My approach to integration testing in distributed systems balances comprehensiveness with practicality. I start by mapping out service dependencies and identifying critical integration points. Then I establish a testing environment that closely mirrors production, using containerization with Docker and orchestration with Kubernetes to ensure consistent test environments.

I implement integration tests that focus on service boundaries and data flow between components. This includes testing both happy paths and failure scenarios, such as network partitions and service unavailability. I use tools like Wiremock for service virtualization and Chaos Monkey principles for resilience testing.

A key aspect of my approach is implementing proper test data management. I maintain dedicated test datasets and use tools like Flyway or Liquibase for database migrations in test environments. I've found this particularly valuable when testing data consistency across services. For observability, I integrate testing with distributed tracing tools like Jaeger or Zipkin to help debug test failures in complex interactions.

For a large-scale application, I would implement a multi-layered authentication system using industry-standard protocols like OAuth 2.0 and OpenID Connect. The core would use JSON Web Tokens (JWTs) for stateless authentication, with refresh token rotation for enhanced security. I'd ensure the system supports multi-factor authentication (MFA) from day one, ideally integrating with standard authenticator apps or SMS as a fallback.

For the infrastructure, I'd implement rate limiting at the API gateway level to prevent brute force attacks, use bcrypt for password hashing with appropriate work factors, and ensure all sensitive data is encrypted at rest and in transit. The authentication service would be isolated in its own microservice to maintain separation of concerns and allow independent scaling.

To handle scale, I'd implement a distributed session management system using Redis clusters for temporary session storage, with careful consideration of token expiration times to balance security and user experience. I'd also implement automated monitoring for suspicious login patterns and integrate with fraud detection systems.

When designing APIs, security needs to be considered at multiple levels. First, I ensure all endpoints use HTTPS/TLS 1.3 for transport security and implement proper certificate management with automatic rotation. I implement robust input validation and sanitization at the API gateway level to prevent injection attacks and XSS, using tools like JSON Schema validation for request payloads.

Authentication and authorization are handled through OAuth 2.0 scopes, with fine-grained permissions for different API operations. I implement rate limiting based on both IP and user tokens to prevent abuse, and use API keys with proper key management for service-to-service communication. All sensitive data in responses is properly encrypted and PII is handled according to data protection regulations.

For logging and monitoring, I ensure sensitive data is never logged in plain text and implement comprehensive audit logging for security-relevant events. I also use security headers like CORS, CSP, and HSTS to protect against common web vulnerabilities, and regularly conduct security audits and penetration testing of the API infrastructure.

In a microservices architecture, I would implement RBAC using a centralized authorization service that maintains role definitions and permissions. This service would expose a gRPC interface for high-performance permission checks and cache frequently accessed permissions using Redis. Roles would be hierarchical, allowing for inheritance and fine-grained permission management.

The implementation would use JWT tokens containing role information, but keeping the payload minimal to avoid token bloat. Each microservice would validate tokens locally using public keys distributed through a secure key management service, with regular key rotation. For complex permission scenarios, I'd implement attribute-based access control (ABAC) alongside RBAC to handle context-specific permissions.

To ensure scalability and maintainability, I'd use a declarative approach to defining roles and permissions in configuration files, version-controlled in Git. Changes to roles would go through a formal review process and be deployed through CI/CD pipelines. I'd also implement comprehensive logging of all authorization decisions for audit purposes and use distributed tracing to track authorization flows across services.

I led a critical migration from a monolithic PHP application to a microservices architecture using Python and Node.js. The monolith was becoming increasingly difficult to maintain and scale, with deployment times exceeding 2 hours and frequent integration conflicts between teams. I developed a phased migration strategy that allowed us to gradually move functionality while maintaining system stability.

First, I established clear architectural patterns and guidelines for new microservices, including standardized API contracts, monitoring, and deployment procedures. We identified natural service boundaries by analyzing business domains and data flow patterns. I created a detailed migration roadmap and worked with product teams to prioritize which services to extract first based on business impact and technical risk.

The migration took 8 months, during which I implemented a strangler pattern using an API gateway to route traffic between old and new services. I also built automated testing and deployment pipelines using Jenkins and Docker to ensure reliability. Through careful planning and execution, we achieved zero downtime during the transition while reducing deployment time to under 15 minutes and improving system reliability by 99.9%.

My mentoring approach focuses on building both technical skills and engineering judgment through hands-on guidance and structured learning. I start by understanding the engineer's current knowledge level and career goals, then create a personalized development plan that includes specific technical areas to focus on and projects that will help build those skills.

I use a combination of pair programming sessions, code reviews, and architecture discussions to transfer knowledge. For example, when mentoring on API design, I'll first explain REST principles and best practices, then have them design an API while I provide feedback. I encourage them to think through edge cases and potential future requirements. I also emphasize the importance of writing maintainable code and comprehensive tests.

Beyond technical skills, I help junior engineers develop critical thinking abilities by having them participate in architectural decisions and system design discussions. I often use the Socratic method, asking probing questions to help them arrive at solutions themselves rather than simply providing answers. This builds confidence and problem-solving abilities that are crucial for career growth.

While building a real-time notification system that needed to handle millions of daily events, I faced a challenging trade-off between data consistency and system performance. The system needed to aggregate user interactions across multiple services and deliver notifications within seconds, but also maintain accurate counts and prevent duplicate notifications.

Initially, I considered using a strictly consistent database approach with transactions, but load testing showed this wouldn't scale to our target throughput of 10,000 events per second. After analyzing our requirements, I made the decision to implement an eventually consistent model using Apache Kafka for event streaming and Redis for temporary state management. This meant accepting a small window (typically 1-2 seconds) where counts might be slightly incorrect, but allowed us to achieve the performance targets.

To mitigate the consistency trade-off, I implemented a background reconciliation process that would periodically sync the real-time counts with our source-of-truth database. I also added monitoring and alerts for any significant discrepancies. This hybrid approach proved successful - we achieved our performance goals while maintaining acceptable consistency for the business use case. The system has been running in production for over a year, handling peaks of 15,000 events per second with 99.99% accuracy.