Senior Data Engineer

FutureFit AI is an AI-powered workforce development company focused on helping people find better jobs and pathways. The Senior Data Engineer will build and own the data foundation, including ingestion and transformation pipelines, warehouse models, analytics datasets, and ML data infrastructure that powers matching and recommendation systems.

Responsibilities:

• 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
• 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
• Build the transformation layer and datasets that power internal analytics, Looker/Quicksight reporting, and the insights we deliver to customers
• 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

Requirements:

• 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
• Experience with jobs-and-skills, HR, or labor market data, or with skills/occupation frameworks such as O\•NET or ESCO
• Experience with hierarchical or taxonomic data — ontologies, classification systems, entity resolution across messy sources
• Experience building data infrastructure for ML: feature pipelines, model deployment and monitoring, or tooling like SageMaker
• Publications, talks, blog posts, or open source work showing your depth in data engineering