Data Engineering Interview Preparation Roadmap

Interview loops differ between companies and teams, but the same kinds of rounds come up repeatedly. Prepare for each kind, then check the specific company’s published information (see company preparation).

How to use this roadmap

• Rank the rounds by how weak you are in each, and spend time in that order.
• Mix daily practice (SQL, coding) with weekly deep work (design, projects).
• Record mock interviews and listen back: most improvements come from clearer explanations.

The plan

1. 01

   Stage 1: SQL round

   Live or take-home SQL: joins, aggregation, window functions, deduplication, edge cases.

   Typical effort

   Ongoing practice

   Outcome

   You write correct queries quickly and talk through edge cases.

   • SQL Data Engineering Cheat Sheet
   • Explain INNER JOIN vs LEFT JOIN with a practical example.
   • How would you optimize a slow analytical SQL query?
2. 02

   Stage 2: Coding round

   Python data processing and easy-to-medium DSA.

   Typical effort

   Ongoing practice

   Outcome

   You solve data-processing problems with clean code and complexity analysis.

   • DSA for Data Engineers: Focused 100–200 Pattern Roadmap
   • How should exceptions be handled in production data pipelines?
3. 03

   Stage 3: Technology deep dive

   Spark, Kafka, Airflow, warehouse platforms: how they work and how they fail.

   Typical effort

   2–3 weeks

   Outcome

   You can explain internals and debugging for the tools on your resume.

   • Apache Spark Interview Cheat Sheet
   • Kafka Cheat Sheet
   • Airflow Cheat Sheet
4. 04

   Stage 4: Data modelling

   Design a schema for a business process; grain, keys, history.

   Typical effort

   1–2 weeks

   Outcome

   You produce a star schema with a stated grain and SCD strategy.

   • Star, Snowflake and Galaxy Schemas and the Grain
   • Slowly Changing Dimensions: Types 0 to 6
5. 05

   Stage 5: System design

   Design a batch, streaming or CDC platform with trade-offs.

   Typical effort

   2–3 weeks

   Outcome

   You run a structured design discussion and defend trade-offs.

   • Data Engineering System Design Cheat Sheet
   • Design a CDC Pipeline from an OLTP Database to the Warehouse
6. 06

   Stage 6: Project deep dive

   Walk through a project: problem, design, decisions, failures, results.

   Typical effort

   1 week

   Outcome

   Two project stories you can tell at two and five minutes.

   • How to Explain a Data Engineering Project in an Interview
7. 07

   Stage 7: Behavioural

   Stories about ownership, conflict, mistakes, and impact, told with real details.

   Typical effort

   1 week

   Outcome

   Six to eight prepared stories mapped to common themes.