Role & responsibilities

- Own data quality for data moving from source systems into analytical datasets, from test design through execution, investigation, and reporting.
- Design, implement, and execute data quality tests, and publish quality metrics and dashboards that producing and consuming groups can use.
- Validate pipelines end to end, covering movement, transformation, completeness, history, freshness, and compliance.
- Write reconciliation, drift, and quality SQL on Snowflake layered models (raw, history, silver, and gold), including slowly changing dimensions and merge behavior.
- Validate PostgreSQL sources with consistent snapshot counts and checks that logical replication kept the expected data.
- Use logical and physical data models and data contracts to assess downstream impact and set quality coverage.
- Automate data comparison and reconciliation in Python with pytest, and keep the checks in GitHub.

Preferred candidate profile





- Senior-level data quality engineering experience in ETL testing and data validation.
- Robust SQL and Snowflake data-modelling experience, including layered raw, history, silver, and gold models, reconciliation and drift queries, and slowly changing dimensions and merge behavior.
- Experience with logical and physical data models and with assessing the downstream impact of schema and data-contract changes.
- Experience validating PostgreSQL sources, including consistent snapshot counts and logical replication.
- Hands-on Python automation for data comparison and reconciliation, using pytest or an equivalent framework.
- Experience running and reading orchestrated pipelines in Airflow or an equivalent scheduler.
- Experience validating data movement, transformation, completeness, history, freshness, and compliance from source systems into analytical datasets.

📌 Data QA Engineer (Hyderabad)
🏢 Dataart Technologies
📍 Hyderabad