Key Responsibilities
Technical Leadership & Strategy
- Define and drive the Data QA strategy, frameworks, and best practices across data platforms.
- Establish scalable QA processes for data pipelines, ETL/ELT workflows, and data products.
- Lead the design and implementation of automated data quality checks and validation frameworks.
- Set standards for data testing, observability, and monitoring.
Hands-On Data QA
- Design, write, and execute complex SQL queries for data validation and root cause analysis.
- Perform deep validation of large-scale datasets, transformations, and aggregations.
- Build and maintain automation scripts/tools for data quality testing.
- Debug data issues across the pipelinefrom ingestion to reporting layers.
- Validate data across systems (source vs warehouse vs downstream applications).
Collaboration & Stakeholder Management
- Work closely with Data Engineers, Analytics Engineers, and Product teams to ensure quality at every stage.
- Partner with stakeholders to define data quality SLAs and acceptance criteria.
- Act as the go-to expert for data quality issues and escalation handling.
Team Leadership & Mentorship
- Mentor and guide Data QA engineers; review their work and elevate team standards.
- Lead by example as a player-coachactively contributing while managing priorities.
- Drive hiring, onboarding, and capability development for the Data QA function.
Quality Governance & Continuous Improvement
- Define and track data quality metrics, KPIs, and dashboards.
- Implement proactive monitoring and alerting for data anomalies.
- Continuously improve QA processes, tools, and frameworks.
Data Management/Mining/Collection
