Data Engineer

We're looking for a mid-to-senior Data Engineer to build the data systems that power our AI agents and insurance decisioning platform. You'll own end-to-end pipeline design and development, co-build ML model pipelines for propensity and cost forecasting, and architect data integrations with client infrastructure. The ideal candidate brings strong data engineering experience, deep curiosity, and a passion for building in a fast-moving, high-ownership environment.

4-9 years of experience required.

• Build robust and scalable data pipelines and infrastructure to power intelligent insurance decisioning by marketers and AI agents.
• Own end-to-end design and development of orchestration workflows - from ingesting raw source data to delivering features for modeling and agent interaction.
• Co-design and implement ML model pipelines to forecast, predict, and recommend propensities, costs, and actions relating to insurance acquisition.
• Architect data integrations between client infrastructure and Fount's Data Platform.
• Contribute to technical leadership in data architecture, modeling approach, and agentic workflow discussions.
• Contribute to the development of our actuarial and AI agent toolkits.

• 4-9 years of experience in data engineering or data science in financial services or related industries.
• Experience building data pipelines for production software systems.
• Batch and streaming frameworks:
  Apache Spark, Kafka, Airflow, dbt.
• Hands‑on with file‑backed SQL engines like DuckDB or Iceberg; understands partitioning, compaction, and schema evolution.
• Strong data wrangling and feature engineering skills across messy, real‑world datasets.
• Familiarity with core risk and finance concepts (retention rates, CLV, loss ratios, underwriting factors).
• Experience building and deploying ML models in production environments.
• Obsessed with AI‑first developer tools (Claude Code, Cursor, Codex) to accelerate development while maintaining strong engineering discipline.

• Experience with marketing and digital customer acquisition.
• Exposure to LLM‑driven analytics over structured data.
• Familiarity with MLOps practices and tools (MLflow, Sage Maker, etc.).