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: 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)