Make data trustworthy, not just present. The dimensions of data quality, testing data in pipelines so bad data fails the build, data contracts and schema evolution, lineage and metadata for 'where did this come from?', governance/security/privacy (PII, masking, access, GDPR), and data observability with freshness and volume SLAs.
Before you start
Familiarity with SQL and a data pipeline helps — this course pairs with the dbt and Data Engineering Fundamentals courses, which build the pipelines this one makes trustworthy.
The Dimensions of Data Quality
"Good data" is not one thing. Learn the measurable dimensions — completeness, accuracy, consistency, timeliness, validity, uniqueness — and how to turn each into a check you can run.
Testing Data in Pipelines
Data tests catch bad data before it reaches dashboards. Assert expectations in the pipeline, quarantine bad rows instead of dropping them, and place tests at the right points in the flow.
Data Contracts & Schema Evolution
Most pipeline breaks come from upstream schema changes nobody warned you about. Data contracts make the interface explicit, and schema evolution rules let data change without breaking consumers.
Lineage & Metadata
When a number looks wrong, the first question is "where did it come from?" Data lineage traces that path, and a data catalog makes datasets discoverable and understandable.
Governance, Security & Privacy
Data engineers handle sensitive data, so protecting it is part of the job. Classify and protect PII, apply masking/tokenization, control access, and meet privacy regulations like GDPR in practice.
Data Observability & SLAs
You cannot fix what you cannot see. Monitor the health of data itself — freshness, volume, schema, distribution — and commit to data SLAs so consumers know what to expect.