Senior Data Engineer, FutureFit

• Strong data engineering experience (roughly 4+ years) designing and operating production ETL/ELT pipelines that other people and systems depend on
• Fluency in Python and SQL, with real depth in SQL — experience in modeling data in a warehouse/data lake, not simply querying it
• Hands-on experience with a modern orchestration and transformation stack (Airflow, dbt, or close equivalents) and with cloud data warehouses
• Experience integrating data from varied external sources — third-party data providers, APIs, flat file feeds — including handling schema changes, unreliable delivery, and inconsistent quality from upstream
• Comfort working with large, messy, inconsistently structured data, and sound judgment about when to clean it, when to model around it, and when to push back on the source
• A builder's instinct for reliability: testing, monitoring, and debugging your own pipelines rather than waiting for someone to report the breakage
• Clear communication: you can explain a data model and its tradeoffs to a non-technical audience

Desirable

• 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
• 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 without 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
• 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.
• We'd rather hire someone with a clear direction they want to grow in than someone who wants to stay in one lane, and we'll build the path with you

• Each journey includes a mix of interviews and a performance challenge. For this role, that might look like:
• Online Application
• Initial Screen with Director of People & Culture
• Interview with Hiring Manager
• Performance Challenge
• Final 1:1 Interviews
• Final Decision
• Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances