Director Data Science Jobs in Orange, CT (NOW HIRING)

Director, Data Engineering - Shelton, CT

Ready to build what’s next with one of the world’s most iconic brands?

Why Join Subway?

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.

About the Role:

The Director, Data Engineering is responsible for leading the design, development, and operation of enterprise data engineering platforms and pipelines that support analytics, reporting, data products, and downstream consumption. This role owns delivery execution, reliability, and scalability of data systems while ensuring alignment with enterprise architecture, security, and governance standards. The Director leads data engineering teams and partners closely with Data Product, Analytics, Platform, and Security leaders to enable trusted, timely, and accessible data across the organization.

Responsibilities include but not limited to:

• Own the data engineering roadmap aligned to data product and business priorities
• Lead the design and build of scalable data pipelines (batch and streaming) and platform services
• Ensure data ingestion, transformation, and delivery are reliable, observable, and performant
• Drive modernization of data platforms and reduction of technical debt
• Partner with Enterprise Architecture and Platform teams to align tooling, patterns, and standards; promote reusable frameworks across teams
• Establish standards for data quality, observability, lineage, and documentation
• Partner with Security, Privacy, and Compliance teams to ensure data protection and regulatory alignment
• Collaborate with Data Product Managers to translate business outcomes into engineering priorities
• Enable Analytics, BI, and Data Science teams with high-quality, well-modeled data
• Lead and develop managers, leads, and senior data engineers; build strong engineering capability through hiring, coaching, and career development
• Define and track KPIs (pipeline reliability, data freshness, SLAs, cost efficiency) and drive operational excellence across data platforms

Qualifications (some examples listed below):

• 8–12 years of experience in data engineering or adjacent platform roles
• 3–5 years of experience leading teams or enterprise-scale data capabilities
• Bachelor’s degree required (Computer Science, Engineering, Data, or related field); advanced degree preferred
• Deep understanding of modern data architectures (cloud data platforms, batch/stream processing)
• Strong experience leading data engineering teams and platforms in a technology organization
• Ability to influence across Product, Analytics, Platform, and Security; proven people leadership and delivery management skills
• Experience operating cloud-based, distributed data platforms in complex, matrixed enterprise environments
• Strong communication skills, executive presence, and a bias for ownership, reliability, and continuous improvement

What do we offer?

• Insurance Plans (Medical, Life)

• Pension/401K/RSP (country specific)
• Competitive Bonus
• Mobility Allowance
• Tuition Reimbursement
• Company Holidays
• Volunteering time
• And More…..

Compensation: The base pay range for this role is $184,500 - $230,600 annually

Pay within this range will be determined in good faith based on job-related factors, which may include skills, experience, education/training, location, and internal equity.