Senior Technical Product Manager - Investor Solutions

Similarweb•New York, NY

17h•$145,000 - $195,000•Hybrid

About The Position

At Similarweb, we are revolutionizing the way businesses interact with the digital world by revealing to them everything that happens online. Our unique data and solutions empower over 6,000 customers globally, including industry giants like Google, Meta, and OpenAI, to make game-changing decisions that drive their digital strategies. In 2021, we went public on the New York Stock Exchange, and we continue to reach new heights! Come work alongside Similarwebbers across the globe who are bright, curious, practical, and good people. We're looking for a Senior Technical Product Manager to design, build and deliver the data products that power Similarweb's Investor Solutions products, alternative data structured for investment research, serving hedge funds, asset managers, and PE/VC firms. This role will report to the VP, Investor Solutions. The Senior Technical PM bridges between our investor clients - quant researchers and fundamental analysts and the datasets built to serve them. Their expertise in data, alongside their finance domain knowledge, makes them the right product professional to translate how funds evaluate, test and use alternative data into scalable data products that clients backtest and trade on. We are looking for a builder first - someone who prototypes and gets his hands dirty, comfortable with raw data, is skeptical by default, and is comfortable with ambiguity: the alt-data-in-the-AI-era playbook is still being written. You will directly impact and steer the work of data scientists, data engineers and analysts by defining what ships, validating it against the raw data, and owning how it reaches clients. You will not hand specs to someone else and wait.

Requirements

3+ years in product or data product roles where the product was the data.
Finance domain: you've worked at or sold to hedge funds / asset managers and understand how alternative data is evaluated, tested and used in an investment process.
Proven product ownership: you've driven discovery → definition → delivery → iteration with cross-functional teams.
Recent experience shipping data products (datasets, datafeeds, APIs) involving diverse data entities and dependencies.
Builder-first, hands-on approach.
Team-oriented mindset with the ability to work effectively with clients, quant researchers, data-sourcing teams, PMs, analysts, DS/DE, GTM and executives.
Excellent communication and presentation skills - verbal and written.
Ability to influence cross-functional teams and align them on what ships and when.
Strong data skills - data analysis, dataset building - from schema definition to data pipelines
Experience building and analyzing data with AI tools and agents
Hands-on Python, comfortable working with big datasets and financial data.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

Nice To Haves

Alternative data experience — vendor side, buy-side data team, or data-sourcing role — is an advantage.
Familiarity with backtesting workflows, factor research, or earnings-preview research is an advantage.
API / MCP / AI-era product experience is an advantage.

Responsibilities

Own investor facing data products end-to-end: Defining the datasets, signals and metrics we sell - from raw assets and pipelines to datasets design, delivery and how clients will extract value.
Own the investor data products strategy and roadmap, identify opportunities, plan them and execute.
Data Quality: Owning coverage, accuracy and freshness as first-class product requirements.
Schema and Delivery Design: Designing schemas and delivery specs for datasets and datafeeds (S3/Snowflake), APIs, and MCP tools.
Hands-on Validation: Working in Python/SQL against raw data to validate hypotheses before committing engineering.
Client Engagement: Working closely with funds, quant researchers and analysts; translating feedback into a roadmap.
Data-backed Prioritization: Deciding what ships based on usage, backtest results and deal feedback.
Guiding Data Development: Working closely with our data science and engineering team to develop the necessary data structures and production datasets based on the roadmap.
Collaborating with Stakeholders: Writing clear requirements, presenting to leadership, and aligning DS/DE/GTM/Analysts on what ships and when.