Partner 22, Fund Strategy Engineering & Data

Founded in Silicon Valley in 2009 by Marc Andreessen and Ben Horowitz, Andreessen Horowitz (aka a16z) is a venture capital firm that backs bold entrepreneurs building the future through technology. We are stage agnostic. We invest in seed to venture to growth-stage technology companies, across AI, bio + healthcare, consumer, crypto, enterprise, fintech, games, and companies building toward American dynamism. a16z has $90B under management across multiple funds.

We’ve established a team that is defined by respect for the entrepreneur and the company-building process; we know what it’s like to be in the founder’s shoes. We’ve invested in companies like Anduril, Airbnb, Coinbase, Cursor, Databricks, Deel, Figma, GitHub, Roblox, SpaceX, and Stripe. Our team is at the forefront of new technology, helping founders and their companies impact and change the world.

The Role

The Fund Strategy Engineering & Data Partner plays a key role in delivering actionable, data-driven insights across all of a16z. This role will leverage Databricks, SQL, and modern software, data, & ML frameworks to construct enterprise-grade datasets, simulations, and analytical tools. The role involves designing automated data pipelines, cleaning and enriching large structured and unstructured datasets, applying advanced statistical and machine learning methods, and developing programmable analyses that address complex business challenges.  This role requires strong technical expertise in software engineering, scalable data systems, and applied analytics, paired with a deep understanding of fund mechanics and investment strategy.

The Partner will collaborate with central teams such as the software group, investor relations, finance, compliance, and tax to transform complex financial datasets into predictive, decision-grade intelligence.

The Fund Strategy team focuses on four core areas:

• New Fund Formation Modeling and Strategy – Developing models to forecast fund dynamics
• Fund Management – Managing deployment pacing, portfolio construction, portfolio strategy, and life-of-fund responsibilities
• Capital Management – Optimizing capital allocation and financial planning
• Cash and Stock Distributions – Strategizing on distribution methods to maximize returns

This position demands proficiency in both the technical aspects of software and data engineering and the strategic understanding of fund management, providing a bridge between data-driven insights and executive decision-making. Must thrive at the intersection of technical execution and business impact. 

This role requires an in-office presence 3 days a week in our San Francisco, CA office or Menlo Park, CA office.

To join our team, you should be excited to:

• Architect and build automated systems, internal tools, and web applications that collect, classify, and analyze financial data across structured and unstructured sources. Work will be done in partnership with the firm’s software group
• Own the end-to-end analytics lifecycle—from exploratory data analysis and hypothesis generation to model development, validation, and deployment
• Guide the technical direction of data and AI initiatives, including architecture, roadmap priorities, and tool selection—applying first-principles thinking to fund analytics
• Lead development of analytical and predictive systems for investment tracking, portfolio optimization, and fund-level strategy
• Design and scale modern data pipelines and ML workflows using Fivetran, dbt, Databricks, and Hex—ensuring performant integration of financial data across cloud systems
• Translate complex datasets into decision-grade insights by applying statistical inference, predictive modeling, and causal analysis for fund pacing, reserve management, and risk assessment
• Deliver real-time intelligence via dashboards, APIs, and simulations—enabling faster, more consistent decision-making across investment and operating teams
• Support pro-rata and follow-on investment analysis with scenario modeling and optimization frameworks
• Conduct public markets analytics using econometrics, natural language processing, and quantitative modeling to inform distribution and exit strategies
• Build and maintain positive relationships and act as a trusted technical and engineering part