Director Performance Engineering Jobs in Bristol, CT

At Subway, we are not standing still. We are building.

This is a business focused on what matters most: growing franchisee profitability, strengthening our brand and creating long-term value. The people who thrive here are the ones who want to make a real impact.

You will not just do the work. You will shape it.

We move fast. We think like owners. We make decisions that matter. We hold ourselves to a high standard because what we do directly impacts thousands of franchisees around the world.

If you bring energy, accountability and a bias for action, you will fit right in.

We take the work seriously, but we also know the best results come from teams that support each other, celebrate wins and show up ready to build something better every day.

This is your chance to be part of what’s next.

The Director, Analytics Engineering is responsible for leading the design, development, and delivery of scalable, high-quality data models, transformations, and curated data assets that power analytics, reporting, and data products across Subway. This role serves as the bridge between Data Engineering and Analytics, ensuring business-ready data is reliable, well-modeled, and governed. Operating within the Technology organization, the Director leads analytics engineering teams and partners closely with Data Engineering, Data Product, BI, and business stakeholders to deliver trusted, performant, and accessible data that enables decision-making at scale.

• Own the analytics engineering roadmap, aligned to data product and business priorities; lead development of curated data models, semantic layers, and analytics-ready datasets; ensure consistency, scalability, and maintainability of data transformations; promote modern data practices including ELT, modular modeling, and version control.
• Define standards for dimensional modeling, data marts, and semantic layers; oversee transformation logic and data quality validation processes; ensure data is structured for analytics, reporting, and downstream consumption; partner with Data Engineering on ingestion and pipeline design alignment.
• Establish data quality standards, testing frameworks, and monitoring practices; ensure clear definitions, lineage, and documentation for key metrics and datasets; support governance initiatives including access control, compliance, and auditing; drive reliability and trust in enterprise data assets.
• Partner with Data Product Managers to translate business requirements into scalable data models; support BI, Reporting, and Analytics teams with curated, performant datasets; collaborate with Platform, Engineering and Architecture teams on tooling and standards; communicate tradeoffs, risks and data limitations clearly to stakeholders.
• Lead adoption and standardization of analytics engineering tools such as dbt or similar frameworks; ensure integration with data platforms including Databricks, Snowflake or equivalent; support CI/CD, testing and deployment processes for data models; promote reusable frameworks and engineering best practices.
• Lead and develop Analytics Engineers and senior ICs; set clear goals, performance expectations, and delivery standards; support hiring, onboarding, and capability building; foster a culture of ownership, data quality, and engineering rigor.
• Define and track KPIs such as data reliability, model performance and user adoption; optimize transformation pipelines and data models for performance and cost efficiency; continuously improve analytics engineering processes and workflows.

• Strong experience in analytics engineering, data modeling, or data engineering roles.
• Deep understanding of the modern data stack including dbt, cloud data warehouses and lakehouses.
• Strong knowledge of SQL, data transformation patterns and data modeling techniques (dimensional modeling, data marts, semantic layers).
• Experience working with BI tools and analytics consumption layers.
• Ability to bridge technical and business needs effectively; strong leadership, collaboration and stakeholder management skills.
• Demonstrated experience driving data quality, governance, and standardization at enterprise scale.
• Bachelor's degree in Computer Science, Data, Engineering or a related field.
• 8–12 years of experience in data engineering, analytics engineering or BI development.
• 3–5 years of experience leading teams or enterprise data initiatives.
• Experience supporting enterprise analytics, reporting and data product environments.
• Experience in cloud-based data platforms such as Databricks, Snowflake, BigQuery or equivalent.

• Advanced degree (Master's) in Computer Science, Data Science, Engineering or a related field.
• Hands‑on experience with dbt (dbt Core or dbt Cloud) at enterprise scale, including package management, macro development and CI/CD integration.
• Familiarity with data mesh principles, federated data ownership and data contract frameworks.
• Experience with data observability and cataloging tools such as Monte Carlo, Great Expectations, Alation or similar.
• Experience in QSR, Retail, CPG or Franchise industry environments.

• Mobility Allowance