Interview Guide
Last-day revision hub. Open this first, read in priority order.
Revision Order
| Priority | Page | Focus |
|---|---|---|
| 1 | 01 - Python | Core Python, OOP, Concurrency, FastAPI, REST, Testing |
| 2 | 02 - AWS | IAM, S3, Lambda, ECS, Glue, Airflow, Architecture |
| 3 | 03 - SQL | Basics, Joins, Window Functions, Performance, Transactions |
| 4 | 04 - Data Engineering | ETL/ELT, Formats, Quality, Pipeline Design |
| 5 | 05 - DevOps & System Design | CI/CD, Docker, System Design Patterns |
| 6 | 06 - JavaScript & React | JS, React Hooks, TypeScript basics |
| 7 | 07 - Behavioral | STAR answers, Behavioral questions |
5-Minute Warm-Up (Read These First)
These are the most likely openers for a senior role:
- Walk me through your most complex or recent project. → Lead with your ETL pipeline / large-scale data platform / AWS architecture story.
- What is a decorator in Python? → Functions that modify behavior without changing source code. Used for logging, auth, timing.
- Explain the difference between a Thread and a Process. → Shared memory / lightweight vs separate memory / heavyweight.
- How would you design a scalable data pipeline? → S3 → Airflow → Glue/Spark → Snowflake/Redshift. Idempotent. Partitioned. Monitored.
- Tell me about a production issue you resolved. → STAR: pick a real incident with measurable resolution.
STAR Method
Use for any behavioral or scenario question.
| Step | Question to answer |
|---|---|
| Situation | What was the context or problem? |
| Task | What was your specific responsibility? |
| Action | What exactly did you do? (most important part) |
| Result | What was the measurable outcome? |
Keep answers to 2-3 minutes. End with the result, not the action.
Interview Logistics
Salary expectations: “I’m targeting Y based on my experience and the market rate for this level.”
Notice period: “My notice period is [X] days and is non-negotiable per company policy. However, I’m happy to discuss an early release date.”
Why are you leaving? → Focus on growth, not criticism. “I’m looking for a role that lets me work more deeply on platform-level engineering at scale.”
Why are you interested in this role/company? → Reference the tech stack alignment (Python, AWS, Data Engineering), the scope of the role, and the company’s mission.
Follow-up tip: After each technical answer, be ready for:
- “Where have you used this in production?”
- “What alternatives did you consider?”
- “How would this behave at 10× scale?”
Gaps (Acknowledged, Not Covered Here)
These topics are high-priority per the JD but have no prepared Q&A in this guide. Fill manually if time allows before the interview:
- SOLID Principles
- Design Patterns (Singleton, Factory, Strategy, Observer)
- Git internals (rebase vs merge, cherry-pick, squash)
- dbt, Snowflake, Redshift deep Q&A
- Behavioral answer scripts (write your own STAR stories)