About the role We are looking for a Data Engineering Team Lead to lead the Data Platform team at DataSnipper. Every decision DataSnipper makes about its products runs through the data platform: which features land, which customers are getting value, what we bill for, what we fix next, and which AI capabilities add the most value. You will lead a team of two data engineers who own the systems that make that possible. That means how usage events get captured across a growing set of products, how they become trustworthy models in Snowflake, and how Customer Success, Product and GTM get to answers without waiting on us. This is a player-coach role in a small, high-ownership team. You are responsible for the people, the roadmap and the technical direction, and you stay hands-on enough to ship alongside the team and make architectural calls credibly. At DataSnipper, Team Lead is our entry point into engineering management: the same people, delivery and product responsibilities as an Engineering Manager, on a smaller team. We are a handful of people serving the whole company, so your judgment about what not to build matters as much as what the team ships. Why DataSnipper? The hardest AI problems aren't about getting an answer fast. They're about getting one people can trust. We believe trust is essential to how society works. Every day, we rely on information that's been independently verified, from financial statements to the products we buy, to the sustainability claims companies make. Behind that trust are audit and finance professionals who examine evidence, verify information and hold organizations accountable. At DataSnipper, we build AI solutions that empower those professionals. By making evidence easier to trace and verify, we help them focus on the judgment and expertise that make their work so important. For our product and engineering team, this is where things get exciting. In audit, a plausible answer isn't good enough. Every output has to be explainable and traceable back to its source. We've just launched a brand new agentic platform, Alwin by DataSnipper. AI agents running in the cloud across hundreds of documents at once, reasoning across PDF, Word and Excel, with a full audit trail behind every step. There are lots of greenfield problems ahead, and real room to shape what we build and how we build it. And we build at scale. More than 600,000 professionals across 3,000+ organizations in 175 countries rely on DataSnipper, including Fortune 500 companies, government agencies and all Big Four audit firms. In 2025 alone, the DataSnipper platform delivered more than $1.4 billion in productivity savings to is customers. Backed by a $100 million Series B led by Index Ventures, at a $1 billion valuation, and as proud members of 1% for the Planet, we care about how we grow, not just how fast. If you want to build AI that professionals trust with work that really matters, this is your chance to help shape the future of audit and finance and do the best work of your career along the way. What your team owns The Data Platform team works across three areas: • Data Platform: reliable, scalable infrastructure that gets the right data to the right place • Internal Analytics: a self-service platform so every team can be data-informed without a ticket • Customer-facing Analytics: the dashboards and exports customers use to see the value they get from DataSnipper Concretely, you would be walking into billions of usage events flowing from our Excel Add-in, web apps and product backends through Azure Event Hubs into Snowflake; a dbt estate built on medallion principles and managed in dbt Cloud; Terraform-managed Snowflake and Azure infrastructure; and a set of product teams shipping AI agents faster than we can instrument them. You will also find real, named open problems rather than a tidy platform: event capture mid-consolidation, multiple methods of user attribution, and a data quality layer that is designed but not yet built. We would rather tell you that up front. What you will do Lead the team • Lead a team of two data engineers through outcomes, not tasks: set clear goals with the team and trust them to figure out how • Make the team measurably better: raise the engineering bar, grow each engineer into bigger problems, and address performance issues directly when they arise • Run regular 1:1s, give clear and timely feedback, and own each engineer's goals and development • Foster a culture of experimentation, learning, documentation and operational excellence • Take part in hiring and help shape the team's scope as it evolves Own direction and delivery • Partner closely with Product, Engineering, Customer Success and GTM to shape the team's roadmap, turning vague data requests into scoped, well-defined work • Make the trade-offs a small team serving the whole company needs: what to build, what to defer, and when a request shouldn't become a pipeline • Set the technical direction and make the architectural calls that shape the team's systems • Keep Snowflake performance and cost, tech debt, dependencies and compliance obligations (audit logging, vulnerability remediation, Vanta evidence) visible and under control • Be the person other engineering teams come to when they need to instrument something new Stay hands-on in the data platform • Contribute to the event ingestion architecture end to end: Azure Event Hubs, Snowpipe, Fivetran, and our shared Python/TypeScript event client libraries • Build and review dbt transformation pipelines that stay reliable as volume, source count and model complexity grow • Define and enforce event contracts and schemas so product teams can instrument new features without silent breakage downstream • Evolve the core data models (event, user, license, company) and integrate new sources such as product backends, MongoDB, HubSpot and billing • Guide reverse ETL and activation work that pushes modeled data back into HubSpot, MongoDB, Postgres and GTM reporting What