About the service
I embed automated data quality checks into your pipelines so that bad numbers never reach dashboards and models. The primary tool is Great Expectations, but I'll use similar frameworks if needed. Instead of learning about a problem at a reporting meeting, you catch it at the moment of table loading or transformation.
At the start, I profile your real tables and talk to the people who use the data, then write checks for what matters: row counts within acceptable bounds, columns without null values, key uniqueness, categories from known sets, correct dates and distributions that shouldn't suddenly shift. I embed these checks into loading and transformation steps — Airflow, dbt, cron, or simple scripts — so a bad batch stops before it breaks reports.
When something fails, you get a clear alert showing the column and rule, plus live Data Docs with history and success rates. I deliver clean, documented code and a runbook so your team can extend the checks themselves.
— Profiling and prioritized list of rules
— Checks in loading and transformations
— Anomaly and freshness alerts to Slack or email
— Data Docs, runbook, and project handoff
At the start, I profile your real tables and talk to the people who use the data, then write checks for what matters: row counts within acceptable bounds, columns without null values, key uniqueness, categories from known sets, correct dates and distributions that shouldn't suddenly shift. I embed these checks into loading and transformation steps — Airflow, dbt, cron, or simple scripts — so a bad batch stops before it breaks reports.
When something fails, you get a clear alert showing the column and rule, plus live Data Docs with history and success rates. I deliver clean, documented code and a runbook so your team can extend the checks themselves.
— Profiling and prioritized list of rules
— Checks in loading and transformations
— Anomaly and freshness alerts to Slack or email
— Data Docs, runbook, and project handoff
