Here’s a 5-hour recap strategy you can use a day before your interview.
🕐 Hour 1: SQL Mastery – Practice, Don’t Just Read
🔍 Focus Areas:
GROUP BY vs. PARTITION BY- Window Functions (
RANK(),ROW_NUMBER()) - Subqueries vs. CTEs
- Aggregate functions with
CASE WHEN - Joins: Inner, Left, Right, Full
- Performance: Indexes, Query Optimization
✅ Recap Method:
- Use LeetCode or StrataScratch: Do 5–7 queries
- Use a whiteboard or paper to write logic without a compiler
- Say your logic out loud as if explaining to an interviewer
🕑 Hour 2: Python and Data Structures
🔍 Focus Areas:
- List comprehension, lambda, map/filter
collections: Counter, defaultdict- File handling, JSON parsing
- Pandas:
groupby,merge,pivot,apply - Algorithms: Sorting, Searching
- Data structures: List, Dict, Set, Heap
✅ Recap Method:
- Practice 5 problems on HackerRank or InterviewBit
- Use notebooks to revisit past mini-projects or utility scripts
- Create a cheat sheet: Write syntax + one-line use case per function
🕒 Hour 3: Apache Spark (PySpark Focus)
🔍 Focus Areas:
withColumnvsselectvsexpr- Transformations vs Actions
- Lazy Evaluation
- Partitioning vs Bucketing
- Broadcast Join, Skew Join handling
- UDF vs Spark SQL functions
✅ Recap Method:
- Write 5 PySpark code snippets from memory
- Watch 10–15 min Spark internals YouTube video (like by DataEngineerThings)
- Revise from your old Spark job script (if available)
🕓 Hour 4: AWS + System Design
🔍 AWS Focus Areas:
- S3, Lambda, Glue, Athena, Redshift
- IAM roles, Policies
- Glue Job Triggers and Partitions
- Event-driven pipelines (S3 → EventBridge → Lambda → Glue)
🔍 System Design Focus:
- How to design a Data Lake
- Batch vs Stream ingestion (Kafka/Kinesis)
- Partitioning & file format (Parquet, Delta)
- Data Pipeline with CDC handling
✅ Recap Method:
- Draw architecture diagrams (paper or tool like Excalidraw)
- Use flashcards for AWS service limits and use cases
- Explain aloud: “How would I build a data pipeline if…”
🕔 Hour 5: Behavioral + Resume-Based Questions
🔍 Focus Areas:
- “Tell me about yourself”
- “What was the most challenging data problem you solved?”
- “Describe a time you optimized a slow query”
- “Have you ever dealt with data inconsistency?”
✅ Recap Method:
- STAR Format: Situation – Task – Action – Result
- Write 3–5 stories covering team collaboration, failures, leadership
- Link them to your resume bullet points
📋 One-Day Quick Recap Checklist
| Area | Time | Key Action |
|---|---|---|
| SQL | 1 hr | Practice queries + whiteboard logic |
| Python | 1 hr | 5 problems + create function cheatsheet |
| PySpark | 1 hr | Code snippets + internals video |
| AWS + Design | 1 hr | Draw 2 diagrams + revise Glue, S3, Athena |
| Behavioral | 1 hr | STAR stories + rehearse resume highlights |
💡 Pro Tips
- Mock Interview: Do one 15-minute mock session with a friend or AI like ChatGPT
- Flashcards: Use Anki for last-minute concepts or AWS trivia
- Avoid Cramming New Topics: Focus on consolidating what you already know
- Rest: A well-rested brain recalls faster and performs better
🔚 Final Thoughts
Recapping for a data engineering interview isn’t about how many questions you can memorize, but how confidently you can explain why and how things work. Stick to your plan, focus on clarity, and practice speaking answers out loud.
🔖 Bookmark Resources for Your Recap
- LeetCode (SQL & Python): https://leetcode.com
- StrataScratch (SQL): https://stratascratch.com
- AWS FAQs & Whitepapers: https://aws.amazon.com/whitepapers
- PySpark Docs: https://spark.apache.org/docs/latest/api/python
- System Design Videos: Search “Data Engineering System Design” on YouTube
Srinimf 