Head of Data Engineering - AngelList

Job Title: Head of Data Engineering
Job Location: San Francisco, CA
Job Listing URL: https://jobs.lever.co/angellist/6592e403-204e-4dc0-a891-65d9e976dfdf
Job Description: About AngelList:

We exist to accelerate innovation. We do this by giving more people the opportunity to participate in the venture economy by building the financial infrastructure that makes it possible for more people to invest in world-changing startups. We also build tools for startup founders that help them run their operations, so they can focus on building their company.

AngelList is the nexus of venture capital and the startup community. We support over $171B+ assets on our platform, and we’ve driven capital to over 13,000 startups. 57% of top-tier U.S. VC deals involve investors on AngelList.

While our scale is large, our ambitions are even larger – we’re innovating on the infrastructure for venture and individual investors and the startups they invest in. Come build with us!

About the Role

Data is the foundation of AngelList’s core products: investor reporting, fund operations, financial intelligence, and compliance. We’re not looking for someone to build dashboards. We’re looking for someone to define the data product itself.

The Head of Data Engineering will own the fidelity, accessibility, and modeling of data across the company. You’ll work side-by-side with engineering, product, operations, and finance to ensure data is not a downstream byproduct, it’s embedded at the core of every workflow we deliver.

This is a rare opportunity to lead a technical domain with real business leverage. You’ll build a team that applies software engineering discipline to data, prioritizes quality over vanity, and treats accuracy as a customer experience. The work you do will enable everything from internal reconciliation to investor-facing insights at scale.
Responsibilities
Architect and maintain high-fidelity pipelines for internal product data and third-party integrations (e.g., regulatory data, banking systems).
Define and manage canonical data models and metrics used across reporting, operations, and product workflows.
Lead data quality efforts: validation, observability, lineage, and SLAs – building trust from ingestion to dashboard.
Partner with engineering to influence schema design and logging at the source.
Build and grow a team of high-agency data engineers and analysts focused on accuracy, reliability, and usability.
Support product teams with experimentation and real-time decision infrastructure.
Deliver self-service, well-documented data interfaces for internal teams and stakeholders.
Set a high bar for precision, accountability, and long-term data stewardship.
What We’re Looking For
8+ years of experience in data engineering, analytics engineering, or data platform roles, with at least 3 years leading teams.
Deep expertise in data modeling, pipeline orchestration, and quality frameworks in a complex domain like venture.
You think like a product owner: you measure success by what data unlocks, not what it looks like.
Strong collaboration skills – can bridge technical, operational, and business domains.
Track record of building from scratch and operating in high-ambiguity environments.
Experience working closely with backend engineering; comfortable reviewing code, schemas, and logging patterns.
High integrity and craftsmanship. You bring precision to naming conventions, documentation, and model definitions.
Familiarity with financial systems, accounting data, or regulated environments is a plus.
How Success will be Measured in this Role:
Data Quality & Trust: Significant improvement in data reliability and observability across critical systems, measured by reduction in data breakages, escalations, and reconciliation errors.
Infrastructure Impact: Delivery of robust, scalable data pipelines and models that power operational, financial, and customer-facing workflows.
Cross-Functional Enablement: High satisfaction from stakeholders (engineering, finance, product, ops) based on access to usable, well-modeled, and timely data.