Data QA Engineer
Core
Design and implement data quality validation frameworks to ensure the reliability, accuracy, and integrity of client data platforms, pipelines, and analytics solutions.
Role type
Data QA Engineer
Builds
Data quality validation frameworks, automated checks, and remediation for client data platforms
Domain
Consulting / Data Engineering / Data Quality
Deliverable
production ML models | product features | dashboards & analysis | client delivery
Required skills
SQL, ETL/ELT testing, data modeling concepts, data warehousing, large-scale data validation, cloud environments (Azure/AWS/GCP)
Preferred skills
Python, data quality tools (Great Expectations/dbt/Deequ), pipeline orchestration (Airflow/Azure Data Factory), CI/CD, data governance tools, Financial Services experience
Technologies
SQL, Python, Great Expectations, dbt, Deequ, Airflow, Azure Data Factory, Purview, Collibra, Informatica
Responsibilities
Design and implement data quality validation frameworks; Validate ETL/ELT processes; Perform source-to-target data validation and reconciliation; Define and execute automated data quality checks; Identify and investigate data anomalies; Collaborate with data engineers and stakeholders to define validation rules; Support testing of data pipelines; Perform root cause analysis for data issues; Contribute to data quality standards and governance; Document validation logic and test scenarios
Seniority
Mid-level (2–5 years experience)
