Manager Data Quality and Operations

Job Description

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

• 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.

• 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.

Must‑have skills & experience

• Hands‑on technical leadership in data engineering, QA/quality engineering and data operations.
• Deep proficiency in SQL, ETL Tools and Python for test automation, data validation, and triage.
• Strong experience with ETL/ELT and orchestration (e.g., Control‑M, Airflow, Databricks Jobs).
• CI/CD pipelines for data (Git/Git Hub, Jenkins/Git Hub Actions) including quality gates and automated regressions.
• Familiarity with MFT platforms and secure file transfer patterns.
• Proven track record building DQ rulesets (profiling, constraints, anomaly detection) and putting them into production with monitoring and alerting.
• Production support experience: incident management, RCA, and post‑mortems with action tracking and verification.
• Strong problem‑solving skills; ability to translate requirements into executable tests and controls.

Nice to have

• Experience with cloud data platforms (Azure/AWS/GCP) and cloud data warehouses/lake houses;
  Databricks strongly preferred.
• Familiarity with data warehousing, dimensional modeling, and performance tuning.
• Exposure Customer 360/MDM and enterprise data governance.
• Experience with BI/semantic layers and data product SLAs.
• Background working with…