Role Summary
Lead efforts to implement new Data Quality standards, emphasizing on modernizing existing ETL pipelines with reusable engineering patterns for implementing Data quality in a "shift left" approach.

Required Skills
Strong ETL/ELT and data engineering experience (Datastage, DBT, Informatica, Snowflake)
Strong SQL + Python skills
Hands-on Data Quality engineering experience
Experience embedding automated DQ checks into pipelines
Strong CI/CD and automated testing knowledge
Understanding of shift-left/inline DQ architecture
Experience designing reusable, scalable pipeline/DQ patterns
Technical leadership and ability to drive adoption across engineering teams

Key Deliverables
DQ-Enabled ETL Pipelines
Implement DQ checks across priority ETL pipelines
Incorporate completeness, validity, accuracy, consistency, uniqueness, and reconciliation checks
Determine appropriate DQ checkpoints throughout the pipeline
Automated DQ Testing & CI/CD Integration
Integrate DQ validation into automated pipeline testing and CI/CD processes
Establish quality gates and thresholds for pipeline promotion
Automate execution and reporting of DQ tests
DQ Failure & Exception Handling
Implement standardized approaches for handling DQ failures
Define reject, quarantine, warning, and pipeline-stop patterns
Enable automated alerting and issue routing
DQ Monitoring & Reporting
Implement monitoring of DQ results within ETL pipelines
Provide visibility into DQ failures, trends, and recurring issues
Capture DQ results and metadata for downstream reporting and governance

Data Quality Analyst

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