Data Engineering Capstone and Interview Preparation
Learn to build three data engineering portfolio projects, document them like a pro, and answer data engineer interview questions with confidence.
What You'll Learn
Understanding What a Data Engineering Capstone Must Prove
A hiring manager in Hyderabad opens the 41st portfolio of the week. Every repository holds a notebook that loads a CSV into pandas and draws a chart.
Building Project 1: A Batch Pipeline with Airflow and dbt
This project proves you can build the most common pipeline in the industry: land data daily, transform it into a model that analysts trust, and...
Building Project 2: A Streaming Pipeline with Kafka and Flink
This project proves you understand time. Batch pipelines can ignore the difference between when something happened and when you saw it.
Building Project 3: A Lakehouse with Quality Gates
This project proves you treat data quality as engineering, not as an afterthought.
Writing the README and Demo Script
A hiring manager spends about two minutes on a repository.
Answering Junior-Level Data Engineering Questions
Junior questions test whether your fundamentals are solid. A strong answer is short, correct, and has one concrete example.
Skills You'll Master
Curriculum Index13 topics
Understanding What a Data Engineering Capstone Must Prove
A hiring manager in Hyderabad opens the 41st portfolio of the week.
Building Project 1: A Batch Pipeline with Airflow and dbt
This project proves you can build the most common pipeline in the industry: land data daily, transform it into a model...
Building Project 2: A Streaming Pipeline with Kafka and Flink
This project proves you understand time. Batch pipelines can ignore the difference between when something happened and...
Building Project 3: A Lakehouse with Quality Gates
This project proves you treat data quality as engineering, not as an afterthought.
Writing the README and Demo Script
A hiring manager spends about two minutes on a repository.
Answering Junior-Level Data Engineering Questions
Junior questions test whether your fundamentals are solid.
Answering Mid-Level Data Engineering Questions
Mid-level questions ask you to own a pipeline.
Answering Senior-Level Data Engineering Questions
Senior questions have no single correct answer.
Solving SQL and Coding Rounds
SQL rounds test whether you can reshape data under time pressure.
Walking Through Two System Design Rounds
Data system design is not "draw Kafka and Spark". It is "show me where the data can be wrong and how we find out".
Running the Definition-of-Done Lab
This lab turns the checklist into a script, so your portfolio repositories are checked the same way every time.
Using the Job-Ready Checklist and a Four-Week Plan
You are ready when you can do these things without notes, not when you have read everything.
Using the Quick Reference and Avoiding Common Mistakes
Keep this section open the night before an interview. It holds the rules and numbers you want ready.
Career Impact
Roles that use the skills in this module.
Data Engineer
Platform Engineer
Next Modules
Practice on the Coding Sheet
Not a software engineer sheet. Every problem comes from real DevOps, SRE, Platform and Cloud interviews, from your first script to a system you build yourself.
Open the Coding SheetFrequently Asked Questions
Three finished projects are enough: one batch pipeline, one streaming pipeline, and one lakehouse with quality checks. Each should run from a single command and have a README that explains the design choices, not just the steps.
No. Everything in this module runs locally with Docker, so it costs nothing. Interviewers care that you can explain the design and the failure cases, and a local project shows that as well as a cloud one.
Most loops include a SQL round, a Python or coding round, a pipeline design round, and a behavioural round. Senior loops add questions on cost, data quality ownership, and how you would roll out standards across teams.