Responsibilities
- Own core pieces of our data lake and analytics stack (e.g., Iceberg, Spark, batch and streaming pipelines) that power demand gen, segmentation, and scoring at scale.
- Design and evolve data systems that balance cost, latency, data freshness, and reliability, making explicit tradeoffs using concepts like CAP theorem, efficient partitioning, and storage layout.
- Partner closely with PM, product analytics, and GTM leaders to shape commercially meaningful solutions: better lead scoring, funnel visibility, audience building, and campaign attribution for marketers and sales.
- Help make Data Hub an AI‑agent‑forward platform, where curated, evergreen datasets automatically feed AI agents and reporting surfaces rather than requiring manual stitching or ad-hoc pipelines.
Work Arrangement
Hybrid
Additional Information
- If you enjoy working at the intersection of data engineering, ML, applied AI, and commercial outcomes, and you like building platforms that make complex data approachable for non-experts, we’d love to talk.
- If you are joining our Engineering team, you will be required to attend a regional HubSpot office for in-person onboarding. If you join our broader Product team, you’ll also attend other in-person events, such as your Product Group Summit and other gatherings, to continue building on those connections. If you require an accommodation due to travel limitations or other reasons, please inform your recruiter during the hiring process. We are committed to supporting candidates who may need alternative arrangements.
