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Automated Data Quality Checks with Great Expectations
Service description
I build automated data quality into your pipelines so that broken numbers never reach a dashboard or a model. My main tool is Great Expectations, but I also use similar frameworks when they fit your stack better. Instead of discovering a problem in a board meeting, you catch it the moment a table loads or a transformation runs.
I start by profiling your real tables and talking to the people who use the data, then I write expectations for what matters: row counts within range, columns that must not be null, keys that stay unique, categories from a known set, sensible dates, and distributions that do not suddenly drift. I wire these checks into your ingestion and transformation steps, whether you run Airflow, dbt, cron, or plain scripts, so a failing batch is stopped before it pollutes reports.
When something breaks, you get a clear alert with the exact column and rule that failed, plus living Data Docs showing every expectation and its pass rate. I hand over clean, documented code and a runbook so your team can extend the checks themselves.
— Profiling and a prioritized checklist of rules
— Expectations wired into ingestion and transformation
— Anomaly and freshness alerts to Slack or email
— Data Docs, a runbook, and a handover session
I start by profiling your real tables and talking to the people who use the data, then I write expectations for what matters: row counts within range, columns that must not be null, keys that stay unique, categories from a known set, sensible dates, and distributions that do not suddenly drift. I wire these checks into your ingestion and transformation steps, whether you run Airflow, dbt, cron, or plain scripts, so a failing batch is stopped before it pollutes reports.
When something breaks, you get a clear alert with the exact column and rule that failed, plus living Data Docs showing every expectation and its pass rate. I hand over clean, documented code and a runbook so your team can extend the checks themselves.
— Profiling and a prioritized checklist of rules
— Expectations wired into ingestion and transformation
— Anomaly and freshness alerts to Slack or email
— Data Docs, a runbook, and a handover session
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