Principal Product Builder, Data

The cost of going from idea to working prototype has collapsed. One person with taste, strong judgment, and AI tools can now do what used to take a team and a sprint cycle. Apollo is hiring for what comes next: Product Builders who combine product judgment, domain depth, and AI-native research and execution.

This role owns Apollo Data, the data layer that is committed to providing the world’s most accurate B2B company, contact, and signal data to power a customer’s GTM stack.

Apollo is expanding from “help me find and message prospects” to “help me run my GTM motion.” That only works if Apollo upholds its commitment to offering the broadest, most accurate, and relevant B2B dataset to power GTM at scale. 

This is a broad, data-centric role that spans data health and telemetry, data source acquisition, evaluation, and integration, measurement of data accuracy and quality, and a keen understanding of how data structure and taxonomy ties to the customer experience. Data is the backbone that makes Apollo the most trusted GTM execution platform in the world.

• The product vision, strategy, roadmap, and delivery for Apollo’s core data catalog 
• Apollo's data as the foundation for the rest of the product: the agent, API, and workflow surfaces that are only as good as the catalog underneath them
• Roadmap prioritization with Data Engineering, Research, and Design, keeping data initiatives tied to company OKRs
• Apollo’s central data telemetry: the metrics and dashboards that define catalog breadth, depth, accuracy, and freshness, built with Data Science and used to drive sourcing and roadmap decisions
• Our always-on data sourcing operation, including source identification from telemetry, evaluation, and integration
• The bridge between R&D and Partnerships on vendor data sourcing, evaluation, and ongoing relationships
• Collaborate with our Engineering team on the latest approaches to upleveling our core data catalog, bringing a product lens and GTM visibility into our data innovations
• Support for key customer data initiatives and evaluations, including direct customer interaction in-tandem with our sales & field teams for all things data
• Cross-functional alignment across Product, Engineering, Design, GTM, Support, and Enablement
• The ongoing evolution of Apollo into the most trusted data layer for sales and marketing

• Get close to customers, GTM teams, Support, and internal users to understand where Apollo’s data has gaps and accuracy or usability issues
• Set product strategy for an always-on motion for upleveling our native data catalog with new data sources
• Set product strategy for how Apollo’s data shows up across the product and in customer systems: enrichment, search and filters, scoring, and the taxonomy and data structure that make the catalog usable at every surface
• Prototype quickly using tools like Claude Code, Cursor, Lovable, Figma AI, and other AI-native workflows to validate ideas before scaling investment
• Turn complex data pipelines into simple, trustworthy customer experiences across coverage, accuracy, freshness, confidence scoring, enrichment waterfalls, and data provenance
• Lead build vs. buy vs. partner decisions for data sources, evaluating vendors on coverage, accuracy, freshness, cost, and compliance, and owning the business case for each
• Partner closely with: 
• Engineering on architecture, reliability, observability, APIs, data models, and edge cases while staying anchored in customer and business outcomes
• Design to make data trust visible: where a record came from, how fresh and confident it is, and what a customer can do when it is wrong
• GTM, Support, and Customer Success to optimize head-to-head data quality evaluations and learn from customer data experiences
• Define the KPI tree and learning loop for the product area, then use it to prioritize, measure, and iterate
• Raise the bar for product clarity by writing crisp strategy docs, PRDs, launch plans, and tradeoff memos that make the next best action obvious

• 7+ years of product management experience, including ownership of complex data-centric product areas from strategy through launch
• AI-native builder. You use tools like Claude Code, Cursor, Lovable, Figma AI, or similar to prototype, validate, and accelerate execution. You can point to things you built or shipped, not just specs you wrote
• Principal-level ownership across a meaningful product area with company-level visibility
• Strong product judgment. You know how to find the highest-leverage problems, make hard tradeoffs, and protect focus
• Data thinker. You can navigate the complexity of many data sources and aggregation processes that centralize data into a high-availability, performant data catalog product; you design and leverage clear data-backed telemetry for driving product decisions
• Compliance fluent. You understand data privacy, GDPR and CCPA, consent, and licensing constraints well enough to design within them and partner effectively with Legal and Privacy
• High agency. You create clarity in ambiguous environments and move work forward without waiting for perfect structure
• Technical fluency across architecture, reliability, observability, APIs, data models, instrumentation, and edge cases
• Customer empathy for how data gaps, stale records, and inaccuracies erode trust for sales, marketing, RevOps, admins, and end users
• Execution excellence. You define, ship, measure, and iterate without thrash
• Strong influence and communication. You align teams, build trust quickly, and make complex systems feel simple

• Experience with B2B data products, data marketplaces, enrichment, identity resolution, or data-as-a-service businesses
• Familiarity with entity resolution, record matching, data freshness and decay, confidence scoring, high-scale data collection and verification
• Experience sourcing, evaluating, and negotiating third-party data vendor relationships, including waterfall or multi-vendor architectures
• Experience building for sales, marketing, RevOps, customer success, or GTM operators
• Experience using analytics, experimentation, observability, or support data to improve data quality and customer outcomes