Sr. Manager Data Foundations

Franchise World Headquarters, LLC; Shelton (CT); On-site; USD 159,000 - 199,000; Full time

Benefits offered by this job

Insurance Plans

Pension/401K

Competitive Bonus

Mobility Allowance

Tuition Reimbursement

Company Holidays

Volunteering time

Job summary

Subway is seeking a Sr. Product Manager, Data Foundation to own the data foundation as a product and ensure enterprise data is connected, landed, and structurally sound for AI-driven outputs.

You will prototype with Claude/Claude Code, define requirements for ingestion, and collaborate with engineering and architecture to build a trusted data backbone. The role emphasizes ownership of data onboarding, quality, and cross-functional alignment, with a focus on real-world impact for thousands of

Qualifications

• 3-5 years in a technical or technical-product role across data pipelines, source systems, data platforms, or technical product management, with a track record of shipping or fixing real data infrastructure, not just describing it
• Experience owning a backlog or roadmap for a data platform or data product and defending the call on what comes next
• Comfort working directly and regularly with external and cross-functional partners, including vendors and internal system owners
• Working SQL fluency and comfort in a modern cloud data platform
• Ships a working prototype alongside a written spec; comfortable using Claude and Claude Code directly to prototype, debug, or explore
• Comfort operating in ambiguity across sourcing, data quality, and analysis, bringing structure where there isn't any yet
• Databricks experience preferred

Responsibilities

• Data foundation roadmap: own the roadmap for how enterprise data sources are onboarded, structured, and kept reliable, in line with the practice's direction, so the platform is a trusted, AI-ready foundation rather than a collection of one-off connections
• Ingestion roadmap and backlog: decide what gets tackled next within ingestion (which sources, which fixes, which requests), sequence it within the practice's priorities, and defend why
• Source connections and vendors: initiate and maintain the relationship with source-system owners, internal and external vendors alike, for new sources and for resolving issues with existing ones. Recommend which sources to prioritize, apply the standards every new source must meet before it lands, and maintain a source register covering each source's owner, data movement, access and history permissions, and refresh frequency (what it can support and what the business needs)
• Structural data quality: ensure ingested data is accurate, complete, correctly typed, observable, and arrives on schedule. Decide how a source is landed and structurally validated before handoff, and whether a source can support a stated requirement as described
• Hands-on prototyping and reusable patterns: prototype directly with Claude and Claude Code alongside written specs, so engineering has something concrete to build from. Contribute to and apply the patterns and quality standards that let each new domain reuse what came before instead of rebuilding it
• Technical partnership: serve as the go-to owner for technical and analytical questions about the data foundation, working with each workstream's engineering lead. Partner with the architecture team on deeper technical evaluation and solutioning and approve its output before it is relied on. Provide technical review, on request, to support definition and certification decisions made by the Principal, Data Partnerships. Flag technical concerns with a proposed definition without deciding it
• Cross-functional alignment: keep engineering, architecture, source-system owners, and vendors aligned on the ingestion roadmap without managing any of them, and help partner teams understand data standards, source onboarding practice, and how to work with the platform well
• Escalation: elevate a source owner who won't engage, and any contractual, legal, or access constraint on extracting, retaining, or using a source's data. Route questions about what a measure means, rather than whether the data can support it, to the Principal, Data Partnerships.

Skills

SQL

Claude

Claude Code

Backlog ownership

Cross-functional collaboration

Tools

Databricks

Claude Code

Job description

Sr. Product Manager, Data Foundation

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 Sr. Product Manager, Data Foundation owns the data foundation as a product: deciding what is needed and what is next for how enterprise data enters and is structured on the platform, so every certified number and AI-driven answer the business relies on rests on data that is connected, landed, and structurally sound. Owns the foundation (not what measures mean), and prototypes directly alongside writing requirements so engineering and architecture can build against something concrete.

Responsibilities include but not limited to:

• Data foundation roadmap: own the roadmap for how enterprise data sources are onboarded, structured, and kept reliable, in line with the practice's direction, so the platform is a trusted, AI-ready foundation rather than a collection of one-off connections.
• Ingestion roadmap and backlog: decide what gets tackled next within ingestion (which sources, which fixes, which requests), sequence it within the practice's priorities, and defend why.
• Source connections and vendors: initiate and maintain the relationship with source-system owners, internal and external vendors alike, for new sources and for resolving issues with existing ones. Recommend which sources to prioritize, apply the standards every new source must meet before it lands, and maintain a source register covering each source's owner, data movement, access and history permissions, and refresh frequency (what it can support and what the business needs).
• Structural data quality: ensure ingested data is accurate, complete, correctly typed, observable, and arrives on schedule. Decide how a source is landed and structurally validated before handoff, and whether a source can support a stated requirement as described.
• Hands-on prototyping and reusable patterns: prototype directly with Claude and Claude Code alongside written specs, so engineering has something concrete to build from. Contribute to and apply the patterns and quality standards that let each new domain reuse what came before instead of rebuilding it.
• Technical partnership: serve as the go-to owner for technical and analytical questions about the data foundation, working with each workstream's engineering lead. Partner with the architecture team on deeper technical evaluation and solutioning and approve its output before it is relied on. Provide technical review, on request, to support definition and certification decisions made by the Principal, Data Partnerships. Flag technical concerns with a proposed definition without deciding it.
• Cross-functional alignment: keep engineering, architecture, source-system owners, and vendors aligned on the ingestion roadmap without managing any of them, and help partner teams understand data standards, source onboarding practice, and how to work with the platform well.
• Escalation: elevate a source owner who won't engage, and any contractual, legal, or access constraint on extracting, retaining, or using a source's data. Route questions about what a measure means, rather than whether the data can support it, to the Principal, Data Partnerships.

Qualifications, Competencies & Traits:

• 3-5 years in a technical or technical-product role across data pipelines, source systems, data platforms, or technical product management, with a track record of shipping or fixing real data infrastructure, not just describing it
• Experience owning a backlog or roadmap for a data platform or data product and defending the call on what comes next
• Comfort working directly and regularly with external and cross-functional partners, including vendors and internal system owners
• Working SQL fluency and comfort in a modern cloud data platform
• Ships a working prototype alongside a written spec; comfortable using Claude and Claude Code directly to prototype, debug, or explore
• Comfort operating in ambiguity across sourcing, data quality, and analysis, bringing structure where there isn't any yet
• Databricks experience preferred

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 $158,900 - $198,600.

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.