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. Youdon'tjust 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'lllead 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,governedsemantics, 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 andlakehousesolutions, 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 AIready data foundations curated datasets, realtime pipelines, featureready 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 selfservice consumption across BI, analytics, and AI use cases
• Establish enterprisegrade 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 thecutting 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 cocreate data products, aligning engineering priorities to business outcomes rather than oneoff 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 andretaintop-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:lakehousearchitecture, DataOps, streaming-first patterns, and data mesh principles
• Drive innovation identifyopportunities 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: Git, CI/CD (GitHub Actions / Jenkins), Infrastructure-as-Code
• BI & Analytics: Power BI, Looker, or equivalent
• Cloud: Azure or equivalent (ADLS, Key Vault, Networking, AAD)