About this role
Location: New York City Work Model: Hybrid (2–3 days in-office) Industry: Enterprise Technology / Infrastructure Compensation: $90,000–$125,000 base salary + equity
About Our PartnerOur partner is an early-stage technology company building enterprise-grade infrastructure for the physical world. As the company scales its go-to-market efforts, data and signal quality are becoming core competitive advantages. The team is investing early in building proprietary data infrastructure to power smarter growth, better targeting, and higher-quality pipeline.
The OpportunityOur partner is hiring a Founding Data Engineer (Growth) to build the data foundation that powers its go-to-market engine. This is a highly impactful, early role focused on turning raw data into actionable signal. You will work closely with Growth, Product Marketing, Sales, and Engineering to design and operationalize systems that improve attribution, targeting, and pipeline quality.
This role is ideal for a technically strong, business-curious data engineer who enjoys working close to revenue and thrives in ambiguous, fast-moving environments.
Responsibilities Build and own attribution models across the GTM stack Design and implement lead scoring and predictive signal generation systems Create and maintain data pipelines that transform third-party datasets into proprietary signals Own tracking infrastructure and instrumentation across marketing, sales, and growth tools Integrate and leverage disparate internal and external data sources to generate high-quality pipeline signal Partner with Growth and GTM teams to ideate, test, and operationalize new data-driven initiatives Ensure data reliability, scalability, and usability across downstream systems
Requirements 3+ years of experience in data engineering, analytics engineering, or related roles Strong proficiency in SQL and Python Experience working with APIs, data warehouses, and ETL pipelines Ability to design and implement data models that support business decision-making Comfort operating in early-stage environments with limited structure Strong problem-solving skills and a bias toward ownership and execution