Sr. Data Engineer

Come join our Data team! High velocity, high trust, and high impact with a will to win. If that resonates deeply with you, this could be your next career move. We're seeking someone who leads with humility, pursues audacious goals, and is motivated by meaningful impact on people and the world. At FutureFit AI, our core mission is to help more people get to better jobs faster and cheaper, with a specific focus on those facing barriers to opportunity. Our work helps resolve the growing issue of economic inequality, ensuring that no one is left behind in the future of work. Our AI-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale. Ready to make an impact? Apply today. Important note: Data shows that men typically apply when meeting 3/10 requirements, while women often wait until it's 10/10. We encourage you to apply if you see a strong (not necessarily perfect) fit. Your Role We're seeking a Sr. Data Engineer to join our team. You will build and own the data foundation of our product: the pipelines, models, and infrastructure that turn raw labor market, skills, and occupation data into the systems that connect people to the right jobs and pathways. This is a hands-on, high-ownership role on a small team. You will design ingestion and transformation pipelines, shape how our data is modeled in the warehouse, make analytics and reporting trustworthy, and build the pipelines that feed our matching and recommendation models in production. You will partner closely with the Engineering, Product, and VP of Data & AI. In this small, nimble team, you will have wide latitude to decide how this platform gets built. What You'll Own Pipelines and platform: Design, build, and operate the ingestion and transformation pipelines that bring labor market, customer, and product data into our warehouse as well as into the product — reliably, on schedule, and at growing scale. Data modeling and quality: Own how our core data is structured, tested, and documented, including the skills, occupation, and career taxonomies at the center of the product. Make data something the whole company can trust witho ut asking first. Analytics enablement: Build the transformation layer and datasets that power internal analytics, Looker/Quicksight reporting, and the insights we deliver to customers. ML data infrastructure: Build and maintain the pipelines that feed our matching and recommendation models, and partner with Engineering and Data Scientists to get models deployed, monitored, and improved in production. Where This Role Can Go This role starts with the platform, but it doesn't end there. The person who builds our data foundation is the person best positioned to shape what we build on top of it — whether that's moving deeper into modeling and the matching systems your pipelines feed, or into the analytical work that turns our data into insight for customers.