Manager, Data Quality and Operations

Domino's•Ann Arbor, MI

31d•Hybrid

About The Position

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. Role: Hybrid (4 Days at Dominos Headquarters, Ann Arbor)

Requirements

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/GitHub, Jenkins/GitHub 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.
BS/MS in Computer Science, Information Systems, Data/Analytics, or equivalent experience.
7–10+ years in data engineering/operations with 3+ years leading QA/DQ or SRE-like functions for data systems.

Nice To Haves

Experience with cloud data platforms (Azure/AWS/GCP) and cloud data warehouses/lakehouses; 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 streaming (Kafka, Event Hubs) and schema management.
Builder’s mindset with bias for automation and measurable outcomes.
Clear, candid communicator; able to translate between engineering detail and business impact.
Empathetic coach who raises the team’s technical bar and problem-solving capability.
Drives a culture of documentation, observability, and continuous improvement.

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.