Partner 22, Fund Strategy Engineering & Data

a16z•San Francisco, CA

10h•$275,000 - $320,000•Hybrid

About The Position

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.

Requirements

Mastery of modern data and software engineering tools and languages, including SQL, Databricks, dbt, Delta Lake, Python, and orchestration or web frameworks
7+ years of experience building scalable, data-driven software systems or internal tools that support financial decision-making, asset management, or portfolio strategy
Degree in Computer Science, Engineering, Data Science, Applied Math/Stats, Finance or similar
Deep domain fluency in venture economics, fund modeling, tokenomics, pro rata mechanics, and carry waterfalls
Proven experience delivering analytical platforms and predictive models that inform executive decision-making
Strong collaborative skills and the ability to work cross-functionally across the various operational groups within a16z
Experience leading projects with company-wide impact
Experience in mentoring/influencing senior engineers across organizations
Proven track record of planning multi-year roadmaps in which short-term projects ladder up to a long-term vision
Experience in driving large cross-functional/industry-wide engineering efforts
Candidate must be authorized to work in and be living in the United States
Low ego, high empathy, and the capacity to collaborate effectively with diverse teams

Nice To Haves

Experience building internal platforms, APIs, or user-facing tools is a plus

Responsibilities

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 partner with key groups of the firm including the investment, legal, capital network, and finance teams
Own the design and delivery of scalable, executive-facing dashboards that integrate data from finance, ops, and engineering to surface insights on fund performance and investment outcomes for senior stakeholders
Support ad-hoc projects related to the Firm’s priorities and initiatives