60-Day Data Engineering Interview Preparation Plan

This plan assumes you already know the fundamentals (see the 30-day fundamentals plan if not) and want to convert them into interview performance.

Rules for the 60 days

• Practise out loud. Interviews test explanation as much as knowledge.
• Time yourself. Speed comes from repetition, not from reading more.
• Keep an error log. Write down every mistake and revisit it at the end of each week.
• Protect rest days. Consistency over eight weeks beats bursts.

The plan

1. 01

   Stage 1: Weeks 1–2: SQL fluency

   Daily timed SQL problems: joins, aggregation, window functions, deduplication, top N per group.

   Typical effort

   10–12 hours a week

   Outcome

   You solve medium SQL problems in under 15 minutes and explain row counts.

   • SQL Window Functions: PARTITION BY, ORDER BY and Frames
   • Find the second-highest salary without using a simple MAX approach.
   • How would you detect and remove duplicate records safely?
   • SQL Data Engineering Cheat Sheet
2. 02

   Stage 2: Weeks 3–4: Python and Spark

   Python data structures and generators; PySpark transformations, joins, shuffles and skew.

   Typical effort

   10–12 hours a week

   Outcome

   You can explain lazy evaluation, shuffles and broadcast joins with examples.

   • Python Data Structures for Data Engineering Interviews
   • What is the difference between a transformation and an action in Spark?
   • When would you use a broadcast join in Spark?
   • What is data skew and how can you mitigate it?
3. 03

   Stage 3: Week 5: Modelling and pipelines

   Star schemas, SCD Type 2, idempotent loads, data-quality checks.

   Typical effort

   10–12 hours

   Outcome

   You can design a schema and explain how a pipeline survives reruns.

   • Star, Snowflake and Galaxy Schemas and the Grain
   • Slowly Changing Dimensions: Types 0 to 6
   • How would you design an idempotent batch pipeline?
   • What are the most important data-quality checks in production?
4. 04

   Stage 4: Week 6: System design

   Practise two case studies end to end, out loud, against a timer.

   Typical effort

   10–12 hours

   Outcome

   You can run a 40-minute design discussion with clear trade-offs.

   • Design a Scalable Batch Data Pipeline
   • Design a Real-Time Analytics Pipeline
   • Data Engineering System Design Cheat Sheet
5. 05

   Stage 5: Week 7: Projects and behavioural

   Prepare two project stories and behavioural stories with real examples.

   Typical effort

   8–10 hours

   Outcome

   You can tell each story in two minutes and handle follow-ups.

   • How to Explain a Data Engineering Project in an Interview
6. 06

   Stage 6: Week 8: Mock interviews

   At least three mock interviews covering SQL, design and behavioural rounds; fix the weakest area.

   Typical effort

   8–10 hours

   Outcome

   You have done timed mocks and closed the biggest gap they revealed.