Data Engineering · Projects

What projects should every Data Engineer build?

7 min read·Beginner
💡
Why projects matter more than certificates

A recruiter can verify a project on GitHub. They cannot verify that you actually understood a certification exam. Projects show problem-solving ability — which is what hiring managers are actually screening for.

The candidates who get shortlisted most consistently are the ones who can pull up a GitHub repo during the interview and walk through why they made specific design decisions. That conversation tells a recruiter far more than a bullet-pointed list of tools.

Here are the projects worth building — ordered from beginner to advanced, with a note on what each one demonstrates.

🌤️
Weather Data ETL Pipeline
Beginner

Pull data from a public weather API, clean and transform it, load into a SQLite or Postgres database. Demonstrates API integration, Python, and ETL basics.

🛒
E-Commerce Sales Analytics Pipeline
Beginner

Process a public dataset (Kaggle), run transformations, and build a reporting schema. Shows SQL skills and data modelling.

☁️
AWS Data Lake Project
Intermediate

Store raw data in S3, process with Glue or Lambda, query with Athena. Shows cloud-native data architecture skills.

🔄
API to Data Warehouse Pipeline
Intermediate

Ingest data from a real API into Snowflake or Redshift, with dbt transformations and Airflow scheduling.

Spark Data Processing Project
Intermediate

Process a large dataset with PySpark. Demonstrates distributed computing knowledge interviewers at product companies look for.

📊
Customer Analytics Dashboard
Intermediate

End-to-end: ingest → transform → warehouse → visualise. Shows you can build something a business would actually use.

🌊
Real-Time Kafka Streaming Pipeline
Advanced

Produce and consume events, process with Spark Streaming or Flink. High-value project for fintech and data-heavy company roles.

🏔️
End-to-End Data Platform
Advanced

Combine ingestion, transformation (dbt), orchestration (Airflow), cloud storage, and monitoring. A full-stack data engineering showcase.

What makes a project interview-ready

A working project is good. A project with a clear README explaining the architecture, the tools used, the design decisions, and the problems you ran into — is much better. Interviewers want to understand how you think, not just what you built. Write that README like you are explaining it to someone who will maintain it after you leave.

Two strong projects beat ten mediocre ones. Depth of understanding is more impressive than breadth of tools listed.

Build portfolio projects during training

Every student at ShifttoTech finishes with real projects they can walk through in an interview.