Data Quality Engineer

Responsibilities

• Define and execute data quality criteria and test strategies for batch and real-time (streaming) data pipelines.
• Build and maintain automated data quality checks across all data layers (medallion architecture), including schema validation, completeness, uniqueness, timeliness, referential integrity, and consistency.
• Proactively monitor and analyze data quality metrics, investigate anomalies, inconsistencies, and defects, and coordinate timely resolution.
• Collaborate within the data engineering and data intelligence squads to implement QA processes in CI/CD for analytics code (e.g., dbt runs / tests).
• Advance data observability practices by developing alerting, dashboards, and SLAs / SLOs for data freshness and correctness.
• Document and validate data lineage (from source to transformation to reporting), ensuring traceability of key fields and metrics.
• Contribute to defining data contracts and acceptance criteria for datasets and reports.
• Support data governance activities (e.g., documenting standards, data contracts, change management protocols).
• Maintain and enhance data quality documentation, including data dictionaries and validation logic.

• 3+ years of experience as a data analyst, data engineer, analytics engineer, or similar role.
• Preferred:
  Educational background in a STEM field.
• Strong SQL skills and direct experience or familiarity with Big Query, MySQL, Postgre
  
  SQL, Databricks, and Click House.
• Hands-on experience with dbt (models, tests, docs, exposures, sources, and test strategies).
• Experience validating datasets in a medallion architecture, and understanding of dimensional modeling (e.g., star schema) and semantic layers.
• Experience or familiarity with BI / analytics platforms such as Apache Superset, Looker, Tableau, Redash, Metabase, or Power
  
  BI.
• Familiarity with both streaming and batch data pipelines; understanding of CDC (Change Data Capture) concepts.
• Proficient in documenting data lineage and data quality test processes.
• Understanding of data observability frameworks, SLAs/SLOs, and incident/root cause analysis for recurring data issues.
• Ability to communicate and collaborate with data, engineering, and other non-technical teams.

• Familiarity with data quality / observability tooling (e.g., Great Expectations, Soda, Monte Carlo, Datadog).
• Experience with orchestration tools (e.g., Airflow) and CI/CD workflows for data pipelines.
• Exposure to data governance practices (data contract documentation, cataloging, and standards).