We are seeking a Data Quality Engineer for a contract role (6+ months) based remotely in the US. In this position, you will design and execute comprehensive data quality strategies across a Lakehouse architecture, partnering with business and engineering teams to ensure data integrity, prevent defects from propagating downstream, and maintain trust in data-driven decision-making.
Responsibilities
Design and execute data quality checks across Bronze, Silver, and Gold layers of the Lakehouse, with a focus on preventing defects from propagating downstream
Partner with business stakeholders to translate requirements into explicit, measurable data contracts before pipelines are built
Identify ambiguities and gaps in business and technical requirements — treating undefined logic as a defect, not a downstream assumption
Build and maintain automated profiling and anomaly detection pipelines using Python and BigQuery
Validate complex ETL/ELT transformations for correctness, completeness, and consistency, including schema drift detection on raw GCS ingestion
Write and review complex SQL to verify transformation logic, aggregations, and business rules across large datasets
Perform deep data analysis to identify trends, anomalies, and root causes of data quality issues
Define and enforce data contracts (schema, nullability, valid value ranges) between data producers and consumers
Communicate data quality issues to non-technical stakeholders in terms of business impact, and work with engineering teams to prioritize remediation
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Audit data access patterns and column-level security policies in BigQuery
Qualifications
Required
Experience with ETL testing
SQL expertise
Data validation experience
Experience in healthcare data environments