Data Quality Analyst
Summary
Develops and maintains data validation processes using SQL and PySpark to ensure data accuracy, while creating executive reports and automated checks in Databricks. Focuses on data quality rules, anomaly detection, and supporting Master Data Management initiatives.
- Build data validation processes with SQL and PySpark
- Conduct data profiling and quality assessments
- Create executive reporting on data quality trends
- Define data quality rules and controls
- Define data standards and business rules with stakeholders
- Design automated data quality checks in Databricks
- Develop data quality scorecards and dashboards
- Document data definitions and lineage
- Embed data quality controls in ingestion and transformation
- Establish data quality SLAs and KPIs
- Investigate data anomalies and root causes
- Monitor data pipelines for quality issues
- Monitor data quality metrics
- Perform root cause analysis and corrective actions
- Support Master Data Management initiatives
- Support audits and financial reporting validation
- Translate business requirements into data quality controls
- Use Databricks monitoring to identify data issues