Manager, Data Quality Engineering

Domino's•Ann Arbor, MI

About The Position

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.

Requirements

8+ years in data engineering, analytics engineering, data quality, or data operations, with 2+ years in a lead, senior lead, or management role.
Demonstrated ability to build, mentor, and develop engineering talent — you know how to grow people, set expectations, and create accountability.
Strong technical judgment across data quality engineering, QA, and production data operations — you can evaluate designs, guide architecture decisions, and hold your team to high technical standards.
Proficiency in SQL and working knowledge of Python/PySpark — enough to review code, guide automation design, and contribute hands-on when needed. You don't need to be the best coder on the team, but you need to be technically credible.
Experience with modern cloud data platforms (Databricks, Delta Lake, Azure Data Lake, cloud data warehouses/lakehouses).
Experience embedding quality into CI/CD workflows — quality gates, automated regression, and release automation for data pipelines.
Experience leading or significantly contributing to incident response, RCA, and reliability improvement in production environments.
Ability to translate technical issues into clear business impact for executive and cross-functional audiences.
BS/MS in Computer Science, Information Systems, Data/Analytics, or equivalent practical experience.

Nice To Haves

Experience with data quality and observability tools (Monte Carlo, Great Expectations, Soda, QuerySurge, or custom frameworks).
Familiarity with orchestration and workflow tools (Control-M, Azure Data Factory, Databricks Workflows).
Experience supporting regulated or high-scale enterprise environments with production SLA governance.
Knowledge of data governance, metadata management, Unity Catalog, and data cataloging.
Experience with streaming data platforms (Kafka/Confluent) and schema management.
Exposure to dimensional modeling, data warehousing, and query performance tuning.
Experience with BI tools, semantic layers, or managing data product SLAs.

Responsibilities

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.
Stay 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.
Partner 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.