As a Manager – Data Quality and Operations focused on enterprise data solutions, your primary responsibility will be to ensure the delivery of high-quality, reliable, and efficient data pipelines and operations across the organization. This is a senior technical leadership role, accountable for the end-to-end data quality engineering and operational excellence of cloud-based data solutions. The ideal candidate will have hands-on experience in large-scale data pipeline management, automated data quality assurance, and production operations, with a proven track record of leading cross-functional teams to drive key decisions and continuous improvement. The Manager – Data Quality and Operations will partner closely with Data Engineering, Platforms, Analytics, and Digital/AI/ML teams to define and implement best practices for data quality, automated testing, and operational support, enabling trusted data activation across the enterprise.
- Location: Domino’s World Resource Center; 30 Frank Lloyd Wright Dr, Ann Arbor, MI 48105
- Shift: Fulltime; Salary
- Job Posting Salary: $140,000-$155,000, plus bonus
- Role: Hybrid (4 Days at Dominos Headquarters, Ann Arbor) Friday, remote
GENERAL RESPONSIBILITIES
Lead Data Quality Engineering & Data QA
- Build quality in: Ensure data pipelines are engineered with quality-first principles and are functionally aligned to business and technical requirements.
- Design quality controls: Define, implement, and maintain automated QA checks for critical data assets with thresholds and SLA-aligned alerting and escalation.
- Enterprise data quality framework: Establish best practices and measurable DQM standards (profiling, validity, completeness, timeliness, consistency, accuracy) across domains.
- Test automation at scale: Drive in-sprint and regression automation for batch and streaming workloads; integrate tests into CI/CD to prevent regressions and accelerate release cycles.
- Coach and develop talent: Lead a pod of QA/Data Quality specialists; raise technical bar in SQL/Python, test design, and root-cause analysis.
Run Data Operations
- Own production SLAs: Monitor and support an extensive footprint of pipelines; ensure uptime and on-time delivery for key datasets, metrics, and downstream products.
- Triage & remediate fast: Lead incident response for data quality/availability issues; drive RCA and corrective actions; reduce MTTR through automation and playbooks.
- Analyze & prevent: Apply EDA to quantify impact (blast radius), identify failure patterns, and implement preventive controls and observability.
- Harden the pipeline factory: Mature CI/CD (branching, approvals, quality gates) and release automation; improve MFT and orchestration flows for reliability and throughput.
- Build the team: Recruit, onboard, and mentor Data Operations Analysts to support enterprise data modernization initiatives at scale.
- Participate in an on-call rotation for critical data products and platform components.