OVERVIEW OF 73 STRINGS: 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. Our 2025 $55M Series B, the largest in the industry, was led by Goldman Sachs, with participation from Golub Capital and Hamilton Lane, with continued support from Blackstone, Fidelity International Strategic Ventures and Broadhaven Ventures.

OVERVIEW OF 73 STRINGS: 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. Our 2025 $55M Series B, the largest in the industry, was led by Goldman Sachs, with participation from Golub Capital and Hamilton Lane, with continued support from Blackstone, Fidelity International Strategic Ventures and Broadhaven Ventures.

About the Role: 73 Strings has been consistently growing and continuously innovating on its product offerings. As we step into the next phase of growth, we are investing in the foundational data platform that powers our AI-augmented products across monitoring and valuation. Today, we have real business demand for data, capable engineers spread across business units, and multiple stakeholders, but no single accountable delivery owner for the data platform itself. This role exists to change that, The Senior Data Product Manager, will be the senior individual contributor who turns the data strategy into shipped product, co-shapes the operating model on the ground, and brings the product discipline and technical credibility needed to make the strategy real. If you have built foundational data products in a low-maturity environment before, and you want to do it again with executive sponsorship and a clear strategic partner, this role is for you. Qualifications: 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. You started your career in a hands-on technical role (data engineering, software engineering, analytics engineering, or similar) and retain that depth. You can read a data model, challenge a pipeline design, and write SQL well enough to validate your own assumptions. Greenfield contribution. You have meaningfully contributed to building a data platform from a low-maturity starting point. Be ready to walk us through what existed when you arrived, what you personally shipped, what you co-shaped, and what survived after you left. Data-driven culture exposure. You have worked in an organisation where data genuinely powered decision-making, so you know what good looks like and have a view on how to get an organisation there. Senior individual contributor. This is an IC role, not a people-management role. You are senior because of how you think and what you ship, not because of headcount. You partner closely with the Product Director rather than driving strategy upwards. Outcome-driven. You measure yourself on adoption, business impact, and decisions changed, not on output, tickets closed, or roadmap completeness. Hands-on. You will write your own specs, draft your own data contracts, and prototype your own analyses when needed. You do not delegate thinking. Challenges the status quo. You name the things others avoid naming. You are diplomatic about it, but you do not let political comfort override the right answer. Comfortable without authority. Most of the engineering capacity you depend on does not report to you. You are 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. You can hold credible technical conversations with senior data engineers and equally credible business conversations with non-technical stakeholders. Responsibilities: You will own delivery of the foundational data platform and core data products that the rest of 73 Strings depends on. Specifically: 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.