As a Manager – Data Quality Engineering, you will lead the organization’s 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 leadership—ensuring data issues are detected early, resolved quickly, and prevented from recurring at scale.General Responsibilities:Leadership, Team Development & Practice Building Own the quality engineering practice end-to-end — vision, strategy, operating model, and roadmap. You are responsible for maturing 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, and retain a high-performing team of 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 repository that replaces tribal knowledge — enterprise test strategy, reusable patterns, and documented standards that scale beyond any individual. Evaluate, adopt, and govern data 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 Technical LeadershipStay close to the work by participating in design reviews, architecture discussions, and technical decision-making — ensuring quality is designed in, not tested in. Guide the team in 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-on to high-impact POCs, automation frameworks, and complex debugging — setting the technical standard through your own work when it matters most. Cross-Functional PartnershipPartner with Data Engineering to ensure pipelines are resilient, observable, and aligned to business requirements. Collaborate with Analytics, Product, and Business stakeholders to align quality metrics to business outcomes. Support AI/ML initiatives by 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.