Senior/Staff Software Engineer (Data Platform)

<div class="content-intro"><h3 class="" data-start="443" data-end="463">About Actively AI</h3> <p><a href="http://www.actively.ai/">Actively AI</a> is defining a new category: Intelligence-Led Revenue.</p> <p>Revenue organizations have always been bottlenecked on human capacity. Reps triage which accounts get attention. Context disappears at every handoff. On any given day, the vast majority of accounts have exactly zero people thinking about them.</p> <p>Actively addresses this at the structural level. Our platform deploys Per-Account AgentsTM across our customers’ TAM, working 24/7 to research, identify opportunities, and advance next steps without being asked. Leading enterprises including Ramp, Ironclad, and Samsara are already making this shift.</p> <p>Our co-founders are former Stanford AI researchers, and the team comes from Harvard, CMU, Berkeley, Brex, Scale AI, and Google. We've raised $68M from TCV, First Harmonic, Bain Capital Ventures, First Round Capital, and more.</p></div><h3><strong>About the Role</strong></h3> <p>We’re looking for a Senior/Staff Data Platform Engineer to build and scale the foundation of Actively’s data ecosystem; the pipelines, transformations, and infrastructure that power every agent, insight, and workflow across the company.</p> <p>Actively's agents make decisions in real time, which accounts to prioritize, what actions to take, when to involve a human. All of that reasoning runs on data: CRM records, call transcripts, external signals, and customer-specific context pulled from dozens of sources. When that data is stale, malformed, or missing context, the agents get it wrong.</p> <p>You'll build and scale the data foundation that every agent, insight, and workflow at Actively depends on; designing pipelines that handle diverse, often messy inputs and turn them into clean, structured, agent-ready representations. At scale, that means millions of accounts, each with their own data shapes, business rules, and edge cases, all needing to stay fresh and reliable.</p> <p>The challenge isn't just throughput. It's building infrastructure that's opinionated enough to enforce quality and consistency, but flexible enough to adapt as new data sources, customer configurations, and agent capabilities keep evolving.</p> <h3><strong>What You’ll Do</strong></h3> <ul> <li><strong>Own the ingestion and transformation layer.</strong> Design and scale pipelines that pull structured and unstructured data from CRM systems, call transcripts, and external signals, normalizing and enriching it into representations agents can reason over in real time.</li> <li><strong>Build for operational use, not just analytics.</strong> The data you pro