Senior Data Product Manager

73 Strings•New York, NY

18h

About The Position

73 Strings is an innovative platform providing comprehensive data extraction, monitoring, and valuation solutions for the private capital industry. The company's AI-powered platform streamlines middle-office processes for alternative investments, enabling seamless data structuring and standardization, monitoring, and fair value estimation at the click of a button. 73 Strings serves clients globally across various strategies, including Private Equity, Growth Equity, Venture Capital, Infrastructure and Private Credit. The company recently secured a $55M Series B funding round led by Goldman Sachs. This role is crucial for the next phase of growth, focusing on the foundational data platform that powers AI-augmented products. The Senior Data Product Manager will be responsible for turning the data strategy into shipped product, co-shaping the operating model, and bringing product discipline and technical credibility to make the strategy a reality. This is an opportunity for someone who has built foundational data products in a low-maturity environment before, with executive sponsorship and a clear strategic partner.

Requirements

Product management track record: 6+ years of hands-on product management experience, of which at least 3 years owning data platforms or data products end to end. General PM experience without data infrastructure depth will be discounted.
Technical foundation: Started career in a hands-on technical role (data engineering, software engineering, analytics engineering, or similar) and retain that depth. Ability to read a data model, challenge a pipeline design, and write SQL well enough to validate own assumptions.
Greenfield contribution: Meaningfully contributed to building a data platform from a low-maturity starting point.
Data-driven culture exposure: Worked in an organisation where data genuinely powered decision-making.
Senior individual contributor: IC role, not a people-management role. Seniority based on thinking and shipping, not headcount. Partner closely with the Product Director rather than driving strategy upwards.
Outcome-driven: Measure self on adoption, business impact, and decisions changed, not on output, tickets closed, or roadmap completeness.
Hands-on: Will write own specs, draft own data contracts, and prototype own analyses when needed. Do not delegate thinking.
Challenges the status quo: Name the things others avoid naming. Diplomatic but do not let political comfort override the right answer.
Comfortable without authority: Most engineering capacity depended upon does not report to this role. Practiced at influence, framing, and earning the right to lead.
Industry: Adjacent industry experience is acceptable. Platform and operating-model experience matters more than domain. Prior exposure to PE/VC, valuations, or financial services is a plus, not a requirement.
Communication: Exceptional written and verbal communication. Can hold credible technical conversations with senior data engineers and equally credible business conversations with non-technical stakeholders.

Nice To Haves

Prior exposure to PE/VC, valuations, or financial services is a plus, not a requirement.

Responsibilities

Own delivery of the foundational data platform and core data products that the rest of 73 Strings depends on.
Translate the data platform strategy into shipped product.
Co-shape the operating model, draft it, socialise it with stakeholders, and make it operational in day-to-day delivery.
Ship foundational data pipelines and flagship data products to production, with named consumers, defined SLAs, and measurable adoption.
Stand up product practices for the data products you own: intake, prioritisation, data contracts, lifecycle, and metrics.
Manage the roadmap for foundational data products, prioritise opportunities across competing stakeholder demands, and maintain existing data products to drive business goals.
Produce high-quality product requirements, in collaboration with the Product Director, SMEs, design, and engineering teams, executing using a lean, problem-first approach.
Stay on top of internal consumer needs through qualitative and quantitative insights.
Identify key opportunities using strong analytical thinking and transform them into action.
Define and execute delivery plans across business units and ensure clear communication with internal stakeholders, particularly the engineering teams currently distributed across BUs.
Provide training and end-user support (primarily to internal teams) during rollout of new data products.