Your role and mission

Data is at the core of Apollo's product.

Our People and Company datasets, and the enrichment fields that make them useful, power nearly every part of the Apollo platform. We believe we already have one of the best B2B datasets in the world. Your mission is to make it the best.

You will own the engineering organization responsible for Apollo's core data platform: how we acquire data, enrich it, understand its quality and freshness, move it across systems, and continuously improve it using the signals generated by millions of Apollo users.

This is one of the highest-leverage engineering leadership roles at Apollo. You will own three critical areas: Native Data, Waterfall Enrichment, and Scraping & Extension, and grow the organization as the platform expands. It’s an incredibly complex problem that involves not only identifying the right datasets and leveraging the information we get from indexing people and company data from across the internet, but also applying the logic that makes sure we always have the right information for every company and user. Also, in data, latency matters – the more fresh the data, the more valuable it is in triggering outreach. As a leader on the team, you will have to deal with significant ambiguity (e.g., what sources should we prioritize and why? How should we think about event data versus relational?) and work relentlessly to make sure our data coverage and quality are always best-in-class.

Data is the lifeblood that makes the execution and intelligence within Apollo work.

What you will do

• Own the engineering strategy and execution for Apollo's core data platform (partnering with Ray Li, Apollo’s CTO and co-founder)
• Lead and grow the teams responsible for Data. Build and develop a world-class engineering team with multiple Principal Engineers across multiple geographies
• Scale the Native Data team from current headcount to the right size to solve the problems described above
• Build systems that leverage signals from Apollo's active users, data network, browser extension, web data, customer CRMs, and other sources to continuously improve our dataset
• Develop a deep understanding of the coverage, quality, confidence, and freshness of every major data vector
• Build systems for identifying when Apollo's data is stale or out of sync and determining when and how it should be refreshed. Work with Sales to understand when we win/lose customers based on our quality. Work on Vendor selection and diversification.
• Create feedback loops across the GTM platform so signals like bounced emails, stale phone numbers, and customer corrections continuously improve Apollo's underlying data
• Own the systems that ingest and distribute data across Apollo's broader platform
• Lead the architecture behind large-scale enrichment, scraping, ETL, reverse ETL, and data synchronization
• Own the monitoring, dashboards, and quality benchmarks that make data freshness and accuracy measurable at scale.
• Join Apollo’s Executive Staff as a key decision-maker and partner with the broader engineering, product, data, and cross-functional teams as a subject matter expert on the long-term strategy for Apollo's most important asset

How we work: AI-native by default

Apollo is an AI-native engineering organization.

You will set the standard for how your teams use AI and agents to move faster across design, implementation, investigation, and operations. More importantly, you will identify where AI can fundamentally change how Apollo acquires, validates, enriches, reconciles, and maintains data at scale. We are looking for leaders who see AI not simply as a productivity tool, but as a new capability for building systems that weren't previously possible.

What we are looking for

• A track record of leading high-performing engineering teams responsible for large-scale data or distributed systems
• Deep technical understanding of data infrastructure, distributed systems, databases, ingestion, synchronization, and data quality
• Experience operating systems where correctness, freshness, reliability, scale, and cost all matter
• Experience building and growing engineering teams while maintaining a high technical bar
• The ability to move comfortably between organizational leadership, product strategy, and deep technical discussions
• Strong judgment around when to build, buy, integrate, or redesign critical infrastructure
• The ability to lead through ambiguity and turn complex technical problems into clear direction for multiple teams
• A record of attracting, developing, and retaining exceptional engineers and engineering leaders
• Genuine, current fluency with AI-assisted development and a clear point of view on how AI changes both engineering and large-scale data systems