Manager, Enterprise Data Engineering

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.

Responsibilities

Design and build scalable, production-grade data solutions across batch and real-time workloads; Design and evolve cloud-based data warehouse and lakehouse solutions with Databricks; Own technical direction for data integration, transformation, and serving layers; Drive streaming data solutions using Confluent Kafka; Lead data modeling, schema design, and optimization across SQL Server, Databricks (Delta Lake), and NoSQL data stores; Establish and enforce engineering standards including code quality, peer reviews, CI/CD, automated testing, documentation, and observability; Build AI-ready data foundations; Partner with Data Science and AI teams to operationalize data pipelines; Define data product contracts; Partner directly with Digital Commerce, Marketing, Supply Chain, Finance, and Enterprise Systems teams; Serve as primary technical point of contact for data domain; Lead, mentor, and grow a team of data engineers; Shape data engineering strategy and roadmap; Evangelize modern data engineering practices; Drive innovation with GenAI, automation, and advanced tooling; Champion a data product operating model; Represent the team in cross-functional forums

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; 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; Experience building data platforms that enable analytics, ML, and AI workloads; 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; BS/MS in Computer Science, Data Engineering, or related field

Nice to have

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

Benefits

Paid Holidays and Vacation; Medical, Dental & Vision benefits that start on the first day of employment; No-cost mental health support for employee and dependents; Childcare tuition discounts; No-cost fitness, nutrition, and wellness programs; Fertility benefits; Adoption assistance; 401k matching contributions; 15% off the purchase price of stock; Company bonus