KEY RESPONSIBILITIES
• Design and implement an enterprise data quality framework covering dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness.
• Define data quality rules, thresholds, and SLAs for critical data domains (customer, product, finance, operations).
• Build and configure automated data quality monitoring pipelines using DQ tooling (Great Expectations, Talend DQ, Informatica DQ, or equivalent).
• Develop data quality scorecards and dashboards for business and technical stakeholders.
• Conduct root-cause analysis on data quality issues and drive remediation through structured workflows.
• Collaborate with data engineers to embed quality checks within data pipelines (ingestion, transformation, serving layers).
• Define and manage a Data Quality Issue Register, tracking defects from discovery through to resolution.
• Partner with governance and catalog teams to integrate quality metrics into the enterprise data catalog.
• Lead data quality working groups and build DQ awareness across data stewards and business users.
Skills
• 4+ years (AM) / 7+ years (Manager–SM) in data quality, data engineering, or data management roles.
• Hands-on experience with DQ tooling: Great Expectations, Informatica DQ, Talend, Monte Carlo, or equivalent.
• Strong SQL skills; experience writing complex data quality validation queries.
• Understanding of data quality dimensions (DAMA-DMBOK) and their measurement methodologies.
• Experience with cloud data platforms (Snowflake, Databricks, Azure Synapse, BigQuery) for DQ pipeline implementation.
• Ability to translate DQ findings into business-impact narratives for non-technical stakeholders.
• CDMP or equivalent data quality certification preferred.