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 dont 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 Dominos 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. 

Youll 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 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 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 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 teams 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: 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) 

Qualifications :

• 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 

Preferred Qualifications

• 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 

Additional Information :

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 
 

All your information will be kept confidential according to EEO guidelines.

Remote Work :

No

Employment Type :

Full-time