Engineering Manager

Question 1

What software engineering experience do you have?

Answer 1

• 18 years experience
• Web application development using c#, asp.net and sql server
• Familiar with engineering tools like jira, confluence

Question 2

What are the engineering best practices you follow?

Answer 2

• Clean code
• Scalability, maintainability, and reusability
• Engineering automation

Question 3

How do you stay up to date with software engineering trends?

Answer 3

• blogs, videos, podcast

Question 4

What is your experience with release planning and coordination?

Answer 4

• End-to-end oversight of the release lifecycle
• Goal is to balance speed with quality, minimize risk, and ensure alignment across business, development, and operations
• coordinate release readiness reviews, ensuring QA sign-off, deployment plans, rollback procedures, and stakeholder communications are in place

Question 5

How would you design a scalable microservices architecture?

Answer 5

• Define microservice based on business capability
• Each service should be independently deployable, stateless and have its own DB
• API Gateway – Acts as a single entry point for clients, handling authentication, logging, and request routing.
• Service Discovery – Enables dynamic service registration and lookup.
• Load Balancer – Distributes traffic efficiently across instances.
• Database per Service – Each microservice manages its own data to avoid bottlenecks.
• Event-Driven Communication – Use message queues (Kafka, RabbitMQ) for asynchronous processing.
  -Containerization – Use Docker and Kubernetes for efficient deployment.
• Auto-Scaling – Adjust resources dynamically based on traffic.
• CI/CD Pipelines – Automate testing and deployment for rapid iteration
• Authentication & Authorization – Implement OAuth, JWT, or API keys.
• Logging & Monitoring – Use tools like Prometheus, Grafana, and ELK Stack

Question 6

How you would optimize database queries for high-traffic applications.

Answer 6

• Indexing: Use appropriate indexes (especially on WHERE, JOIN, and ORDER BY columns) to speed up lookups.
• Query Optimization: Rewrite complex queries to be more efficient—avoid SELECT *, unnecessary subqueries, and large joins.
• Caching: Implement query result caching (e.g., Redis or Memcached) to avoid repeated hits on the database.
• Connection Pooling: Use connection pooling to manage and reuse database connections efficiently.
• Denormalization & Materialized Views: In read-heavy systems, denormalize or use materialized views to reduce join operations.

Question 7

What are the trade-offs between monolithic and microservices architectures?

Answer 7

Monolithic Architecture

Pros:

• Simpler to develop and deploy initially.
• Easier to test as everything is in one codebase.
• Less overhead in terms of inter-service communication.

Cons:

• Hard to scale individual components—must scale the entire app.
• Becomes difficult to maintain and evolve as it grows.
• A small change can require redeploying the entire application.

Microservices Architecture

Pros:
- Independent scalability and deployment of services.
- Better fault isolation—a failure in one service doesn’t crash the whole system.
- Enables tech stack flexibility per service.

Cons:
- Higher complexity in communication, deployment, and data consistency.
- Requires robust monitoring, logging, and orchestration (e.g., Kubernetes).
- More effort in managing service contracts and versioning.

Question 8

What strategies do you use to prioritize technical debt vs. new features?

Answer 8

1) Impact analysis of tech debt
2) Collaborate with dev team to understand the cost of unresolved debt
3) Allocate 20-30% of backlog capacity for addressing tech debt
4) Prioritize debt and feature using WSJF
5) Communicate to stakeholders on the trade off

Question 9

How would you design a CI/CD pipeline for a large-scale application?

Answer 9

1) Source Control Integration

• Use Git (e.g., GitHub, GitLab) with clear branching strategies

2) Continuous Integration (CI)

• Trigger builds automatically on code commits.
• Run unit tests, static code analysis, and linting.
• Use tools like Jenkins, GitHub Actions, or GitLab CI.

3) Build & Artifact Management

• Package builds into Docker containers or deployable artifacts.
• Store in a central artifact repository (e.g., Nexus, Artifactory).

4) Automated Testing

• Run integration, API, UI, and security tests in dedicated test environments.
• Use parallel test execution to save time.

5) Continuous Deployment (CD)

• Promote builds to staging and production via automated pipelines

6) Environment Management

• Use infrastructure as code (IaC) tools like Terraform or Ansible.
• Container orchestration via Kubernetes for consistent environments.

7) Monitoring & Alerts
- Integrate with tools like Prometheus, Grafana, ELK, and Sentry to monitor pipeline health and application performance