As a data engineer, ensuring data integrity and quality is a big part of my work. I usually approach it at multiple stages of the pipeline. First, I put strong validation checks at the ingestion layer: for example, schema validation, data type checks, and constraints on nulls or duplicates. If the incoming data doesn’t meet those rules, it gets flagged or sent to a quarantine table for review instead of polluting the main dataset.
Next, I implement data quality tests within the transformation layer using tools like Great Expectations or debt tests to make sure the data behaves as expected, things like value ranges, referential integrity, and completeness.
I also maintain data lineage and monitoring through tools like Airflow or DataDog, so I can quickly detect anomalies, failed jobs, or unexpected volume changes. On top of that, I work closely with analysts and stakeholders to define clear data quality metrics and thresholds.
Finally, data engineers like me document everything, from source assumptions to data contracts, so any changes upstream or downstream don’t break the integrity of the pipeline. It’s really about combining automation, validation, and collaboration to keep the data clean and trustworthy.
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