Data Quality Engineering: Design, develop and implement SQL and Databricks validations aligned to business rules and quality standards, ensuring completeness, consistency and reliability across shared data platforms.
Reusable Frameworks and Automation: Build modular scripts, utilities and reusable components in Python to scale data validation and monitoring across multiple pipelines and products.
Monitoring and Reporting: Create and maintain Power BI dashboards, critical metric scorecards and reports that give customers self-service insight into data quality performance, SLA alignment and data health trends; provide clear narratives and alerts that drive action.
Issue Triage and Root Cause Analysis: Lead investigations across source systems, ingestion pipelines and transformation layers to identify anomalies and systemic issues; partner with engineering and upstream teams to implement remediation and preventative controls.
Data Governance and Documentation: Maintain robust documentation for data quality rules, validation logic, data contracts and critical metric definitions; support metadata tagging, taxonomy and ontology alignment to enable AI-ready data products.
Customer Partnership and Enablement: Work closely with data product
managers and multi-functional teams to embed quality by design, prioritize backlogs and communicate outcomes that influence decision-making.
Continuous Improvement and Standards: Standardize guidelines for data quality across cloud platforms, continuously improve performance and cost efficiency, and help set enterprise-wide quality benchmarks that scale with growth.