Data Engineering Projects (Real Pipelines, Real Portfolio)

The best data engineering projects look like real pipeline work. These are real data systems you fix in a live cloud workspace - a non-idempotent load, a slow report, a broken dbt model - each one a portfolio piece.

Why these beat a Kaggle notebook

A notebook shows analysis; data engineering is about reliable pipelines and correct data at scale. These practice exactly that, the part that gets you hired as an engineer.

18 projects to start with

Build a Kimball Star SchemaData · Senior Wire Data Quality Tests Across a dbt LineageData · Senior Handle Parquet Schema Evolution (v1 + v2)Data · Senior Build an Exactly-Once Kafka PipelineData · Senior Switch Batch ETL to Streaming on AWS KinesisData · Senior Build a Data Quality Monitoring APIData · Mid Schedule a Pipeline as an Airflow DAGData · Mid Build a Staging-to-Marts dbt ProjectData · Mid Implement SCD2 With dbt SnapshotsData · Mid Migrate a pandas Pipeline to PolarsData · Mid Stream a Kafka Topic to ParquetData · Mid Implement the Outbox Pattern (At-Least-Once)Data · Mid Backfill 90 Days of Events With AirflowData · Mid Make an ETL Pipeline IdempotentData · Junior Speed Up a Slow SQL Report QueryData · Junior Make a CSV Importer Resilient to Bad DataData · Junior Handle Orphan Records in an ETL JoinData · Junior Debug a Broken SQL Revenue AggregationData · Junior

Pick by the skill to show

Reliability? The idempotency and pipeline ones. SQL depth? The window-function and tuning ones. Each finished fix is evidence on your portfolio.

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