Manager, Enterprise Data Engineering

Job Description 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. General Responsibilities Technical Leadership 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 Stakeholder Partnership 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 Team Leadership & Growth 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 Thought Leadership 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 Tech Stack Cloud Data Platform: Databricks (Delta Lake, Unity Catalog, Workflows, SQL Warehouses) Streaming: Confluent Kafka, Kafka Connect, Schema Registry Databases: SQL Server, NoSQL (MongoDB / Cosmos DB / DynamoDB) ETL / Orchestration: Talend, Databricks Workflows, Azure Data Factory Languages: Python, PySpark, SQL DevOps: Gi