Data Engineer

You’ll be joining a small, high-impact data engineering team within Federato’s AI/ML organization. Our focus is on building the infrastructure and internal frameworks that empower machine learning engineers to develop, deploy, and iterate on AI-powered features ranging from prompt-based LLM workflows to more traditional model-driven systems. We collaborate closely with ML, analytics, and product teams to ensure data and tooling are reliable, scalable, and aligned with the needs of our AI-native platform.

What You’ll Be Doing:

• Collaborate with Data Science, Product Managers and Software Engineers to build robust ETL pipelines that enable the Product Support team to deliver compelling user-facing features
• Contribute to architecture decisions, observability tooling, and data quality initiatives that keep our platform robust and maintainable.
• Contribute to a scalable internal framework for managing prompt engineering pipelines and other AI workflows.
• Enforce and elevate engineering best practices across the AI/ML org, including code quality, testing, and documentation.

Who We Hope You Are:

• 5+ years of experience in data engineering, backend engineering, or related roles with a focus on data infrastructure.
• Proven experience designing and maintaining scalable data pipelines (e.g., using Airflow, Dagster, or Prefect).
• Experience with software development practices like version control, CI/CD, or dbt testing strategies.
• Strong proficiency in SQL and Python, with bonus points for Typescript (or similar) experience
• Comfort working with version control, CI/CD systems, and cloud infrastructure (e.g., AWS, GCP, Terraform).
• Comfortable navigating ambiguity and working closely with business stakeholders to understand their data needs.
• Proven track record of designing high-impact data products and pipelines in fast-paced environments.

Bonus Points for:

• Prior experience working in or adjacent to insurance, fintech, or risk modeling domains.
• Prior exposure to ML ops or experience supporting AI/ML-driven products
• Enthusiasm for building internal tools or frameworks to improve team velocity.
• Contributions to open-source data tools or involvement in the data community.