Manager, Data Quality Engineering

Domino's•Ann Arbor, MI

21h•Onsite

About The Position

You are a technical engineering leader first. You can architect an end-to-end streaming solution, debug a complex Spark job in production, and present a data strategy roadmap to VPs — all in the same week. You don't just manage engineers; you make them better. You set the technical bar, own critical data domains, and serve as the go-to authority when the hardest problems land on the table. You will design, build, and scale the data pipelines that power Domino's — integrating batch and real-time data across Digital Commerce, Marketing, Supply Chain, and Finance to deliver trusted, high-quality data products that drive decisions at every level of the business. You'll lead data engineering with a data-as-a-product mindset — delivering data products end-to-end, from ingestion and transformation to semantic modeling, quality, and serving. Each data product has clear consumers, defined SLAs, governed semantics, and measurable business outcomes.

Requirements

8+ years of hands-on data engineering experience; 3+ years leading engineering teams
Deep technical expertise with at least one major cloud data platform — Databricks strongly preferred
Production experience building and operating streaming data solutions (Confluent Kafka or equivalent)
Strong proficiency in Python, PySpark, and SQL — you can still architect and debug production pipelines
Experience with SQL Server, cloud data warehouses, and NoSQL databases in enterprise environments
Experience with Customer 360 platforms, identity resolution, and unified customer data solutions — building the data engineering foundations that power a single, trusted view of the customer
Experience building data platforms that enable analytics, ML, and AI workloads — even if you are not training models yourself
Strong understanding of how data engineering, semantics, and data quality directly impact AI outcomes
Proven ability to partner with business stakeholders and translate ambiguous requirements into scalable technical solutions
Track record of building, growing, and retaining high-performing engineering teams
Excellent communication — you can go deep in a design review and go broad in a leadership presentation
BS/MS in Computer Science, Data Engineering, or related field

Nice To Haves

Familiarity with MarTech stacks — CDPs, campaign analytics, audience segmentation data flows
Talend ETL development and cloud migration experience
Data governance and compliance (SOX, CCPA/GDPR)
Databricks certifications (Data Engineer Professional, Associate)
Exposure to ML/AI data foundations: feature stores, MLflow, experiment tracking
QSR, retail, or high-volume consumer-facing industry experience
Experience driving Agile/Scrum delivery in matrixed organizations

Responsibilities

Design and build scalable, production-grade data solutions across batch and real-time workloads — you set the technical bar for the team
Design and evolve cloud-based data warehouse and lakehouse solutions, with Databricks as the core platform
Own the technical direction for data integration, transformation, and serving layers across your domain
Drive streaming data solutions using Confluent Kafka for real-time use cases — POS transactions, digital order events, customer activity, and supply chain signals
Lead data modeling, schema design, and optimization across SQL Server, Databricks (Delta Lake), and NoSQL data stores
Establish and enforce engineering standards: code quality, peer reviews, CI/CD, automated testing, documentation, and observability
Design, build, operate, and continuously improve data assets that are reliable, discoverable, and ready for analytics and AI
Build AI‑ready data foundations — curated datasets, real‑time pipelines, feature‑ready data, and governed semantics that accelerate ML and GenAI use cases
Partner with Data Science and AI teams to operationalize data pipelines that move models from experimentation to production
Define data product contracts (schemas, freshness, quality, semantics) that enable self‑service consumption across BI, analytics, and AI use cases
Establish enterprise‑grade semantics to ensure consistent definitions across Digital Commerce, Marketing, Supply Chain, and Finance
Evaluate and adopt emerging technologies — staying hands-on and keeping the team at the cutting edge
Partner directly with Digital Commerce, Marketing, Supply Chain, Finance, and Enterprise Systems teams to understand business needs and translate them into scalable engineering solutions
Serve as the primary technical point of contact for your data domain — owning requirements intake, solution design, and delivery
Collaborate with Data Architecture, Data Science, Analytics, and Platform teams to align on standards, governance, and shared data products
Drive data activation and enablement — making data accessible, discoverable, and actionable for downstream consumers
Partner with business stakeholders to co‑create data products, aligning engineering priorities to business outcomes rather than one‑off data requests
Lead, mentor, and grow a team of talented data engineers — build a culture of ownership, technical excellence, and continuous learning
Conduct design reviews, architecture discussions, and hands-on pairing sessions that elevate the entire team's craft
Drive career development, leveling frameworks, and growth plans that help engineers reach their full potential
Manage resource allocation across projects — balancing modernization, new feature delivery, and operational support
Recruit and retain top-tier engineering talent — your technical credibility is the strongest hiring signal
Shape the data engineering strategy and roadmap — presenting architecture decisions, migration plans, and business impact to senior leadership
Evangelize modern data engineering practices: lakehouse architecture, DataOps, streaming-first patterns, and data mesh principles
Drive innovation — identify opportunities to leverage GenAI, automation, and advanced tooling to accelerate engineering velocity
Champion a data product operating model — moving the organization from pipeline delivery to product ownership, reuse, and scale
Influence how teams define success: adoption, trust, and business impact — not just pipeline completion
Represent the team in cross-functional forums, architecture review boards, and vendor engagements