Manager Data Quality Engineering
As a Manager Data Quality Engineering, you will lead the organizations data quality, quality engineering, and data analyst practice. This is a senior technical leadership role accountable for ensuring the reliability, trustworthiness, and operational excellence of data pipelines and data products across analytics, AI, and operational platforms.
You will partner closely with Data Engineering, Platform, Analytics, Product, and Business teams to embed quality-by-design into data pipelines, implement automated testing and observability, and run production data operations. The role combines proactive quality engineering with hands-on operational leadershipensuring data issues are detected early, resolved quickly, and prevented from recurring at scale.
General Responsibilities:
Leadership, Team Development & PracticeBuilding
- Own the quality engineering practice end-to-end vision, strategy, operating model, and roadmap. Youare responsible formaturing QE from a support function into a core engineering discipline.
- Partner with Data Engineering to ensure pipelines are resilient, observable, and aligned to business requirements.
- Build, develop, andretaina high-performing teamof quality engineers and analysts (onshore + offshore). Set clear expectations, provide regular feedback, and create growth paths for your team members.
- Define and govern QE standards, processes, and KPIs including automation coverage, cycle time, defect leakage, test effectiveness, and data validation coverage across all Lines of Business.
- Establish a culture of engineering rigor and accountability where quality is everyone's responsibility, not a gate at the end of the pipeline.
- Create a knowledge repositorythat replaces tribal knowledge enterprise test strategy, reusable patterns, and documented standards that scale beyond any individual.
- Evaluate, adopt, and governdata quality and observability tools (build vs. buy) e.g., Great Expectations, Soda, Monte Carlo,QuerySurge, or custom Databricks-native frameworks.
- Build quality into data pipelines through preventive design, automated testing, and CI/CD quality gates.
- Design and maintain automated checks for freshness, completeness, accuracy, validity, volume, and schema drift.
- Establish enterprise data quality frameworks, scorecards, SLAs/SLOs, and standards for critical datasets.
Hands-On TechnicalLeadership
- Stay close to the workbyparticipatingin design reviews, architecture discussions, and technical decision-making ensuring quality isdesigned in, not tested in.
- Guide the teamin building automated data validation frameworks (Python,PySpark, SQL) covering data comparison, regression, BI report validation, and pipeline smoke tests.
- Drive the embedding of quality gates into CI/CD pipelines freshness, completeness, accuracy, validity, volume, schema drift, and business rule conformance checks before production deployment.
- Architect and oversee data quality observability dashboards, alerting, SLA-aligned thresholds, and escalation paths for engineers, product owners, and leadership.
- Lead incident response for critical data quality issues guide triage, RCA, post-mortems, and corrective actions. Reduce MTTR through automation and operational playbooks.
- Selectively contribute hands-onto high-impact POCs, automation frameworks, and complex debugging setting the technical standard through your own work when it matters most.
Cross-Functional Partnership
- Partner with Data Engineeringto ensure pipelines are resilient, observable, and aligned to business requirements.
- Collaborate with Analytics, Product, and Business stakeholdersto align quality metrics to business outcomes.
- Support AI/ML initiativesby ensuring reliable, high-quality training and inference data.
- Work with platform teams(Databricks, Azure, CI/CD tooling) to embed quality signals natively into orchestration and release workflows.
