Sr. Manager Data Foundations

Subway•Shelton, CT

19h

About The Position

The Sr. Product Manager, Data Foundation owns the data foundation as a product, deciding what is needed next for enterprise data to enter and be structured on the platform. The role ensures that certified numbers and AI-driven answers rely on connected, landed, structurally sound data. It owns the foundation rather than the meaning of measures, and prototypes directly alongside writing requirements so engineering and architecture have concrete materials to build against. The position owns the roadmap and backlog for onboarding, structuring, and maintaining enterprise data sources; builds relationships with source-system owners and vendors; establishes source standards and a register of source ownership, data movement, access and history permissions, and refresh frequency; and ensures data is accurate, complete, correctly typed, observable, and timely. It prototypes with Claude and Claude Code, develops and applies reusable patterns and quality standards, and serves as the technical and analytical point of contact for the data foundation. The role partners with engineering and architecture, reviews and approves architecture output before it is relied upon, supports definition and certification decisions with technical review, aligns cross-functional partners without managing them, and escalates engagement or contractual, legal, and access constraints. Questions about what a measure means are routed to the Principal, Data Partnerships.

Requirements

3–5 years in a technical or technical-product role across data pipelines, source systems, data platforms, or technical product management.
Track record of shipping or fixing real data infrastructure, rather than only describing it.
Experience owning a backlog or roadmap for a data platform or data product and defending decisions about 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 with a modern cloud data platform.
Ability to ship a working prototype alongside a written specification; comfort using Claude and Claude Code directly to prototype, debug, or explore.
Comfort operating amid ambiguity across sourcing, data quality, and analysis, and bringing structure where none exists.

Nice To Haves

Databricks experience.

Responsibilities

Own the roadmap for onboarding, structuring, and maintaining reliable enterprise data sources in line with the practice’s direction.
Own the ingestion roadmap and backlog, determine sequencing of sources, fixes, and requests, and explain and defend prioritization decisions.
Initiate and maintain relationships with internal and external source-system owners and vendors for new sources and existing-source issues.
Recommend source priorities, apply standards that sources must meet before landing, and maintain a source register covering ownership, data movement, access and history permissions, and refresh frequency.
Ensure ingested data is accurate, complete, correctly typed, observable, and delivered on schedule.
Decide how sources are landed and structurally validated before handoff, and assess whether a source can support a stated requirement.
Prototype directly with Claude and Claude Code alongside written specifications so engineering can build from concrete examples.
Contribute to and apply reusable patterns and quality standards for onboarding new data domains.
Serve as the go-to owner for technical and analytical questions about the data foundation, working with each workstream’s engineering lead.
Partner with architecture on deeper technical evaluation and solutioning, and approve its output before it is relied upon.
Provide technical review, when requested, to support definition and certification decisions made by the Principal, Data Partnerships; flag technical concerns without deciding proposed definitions.
Keep engineering, architecture, source-system owners, and vendors aligned on the ingestion roadmap without managing them.
Help partner teams understand data standards, source onboarding practices, and how to work effectively with the platform.
Escalate source owners who will not engage and contractual, legal, or access constraints on extracting, retaining, or using source data.
Route questions about the meaning of a measure to the Principal, Data Partnerships.