Data Quality Automation Engineer

Our Randstad client in Washington, DC is seeking a Data Quality Automation Engineer to lead the design and implementation of automated data validation, profiling, and anomaly detection pipelines. This role will play a critical part in ensuring data integrity across modern data platforms including Databricks, SAP DataSphere, and Informatica Cloud. The ideal candidate will have strong technical skills in Python and SQL, a solid understanding of data governance, and experience working cross-functionally with engineering, governance, and business teams to embed quality and compliance into every stage of the data lifecycle.

Key Responsibilities:

• Design and implement scalable, automated data quality pipelines to proactively identify and resolve data issues
• Build reusable components for anomaly detection, rule enforcement, and schema validation
• Translate data governance policies (e.g., certified sources, critical fields) into enforceable technical rules
• Integrate quality checks across tools like Databricks, SAP DataSphere, and Informatica Cloud, while remaining adaptable to evolving tech stacks
• Partner with data engineers, governance teams, analysts, and product owners to align quality expectations and deliver trusted data
• Track, document, and report on data quality metrics, including rule compliance and issue resolution
• Leverage tools such as Great Expectations or AWS Deequ to implement data quality frameworks
• Use Python and SQL to create profiling scripts and validation logic
• Support enterprise adoption of certified datasets and enforce ownership and stewardship rules