Senior Data Engineer

Float builds resource management software for delivery teams. The Senior Data Engineer will own production-grade, ML-ready data pipelines that power Intelligence features, maintain the data warehouse and infrastructure, and help evolve the platform toward event-driven architecture.

Responsibilities:

• Intelligence pipeline ownership: You'll take end-to-end ownership of the data pipelines that power NextGen and other Intelligence team features
• Applied ML in production: You'll contribute to AI features in production, building the data infrastructure that makes those features work reliably
• Pipeline health and maintenance: Working alongside Davi and Roberto (the Data team), you'll ensure our pipelines and data infrastructure are reliable and well-documented
• Event-driven architecture: As Float's data needs evolve, you'll play a key role in how we introduce event streaming (Kafka/Debezium) into the stack, and will have the opportunity to contribute to architectural decisions, as well as implementing them
• Scheduling optimization work: You'll develop familiarity with Timefold and Float's scheduling domain
• Cross-team data impact: Improvements to pipelines and infrastructure tend to ripple outward. Over time, you'll likely have opportunities to improve workflows beyond the Intelligence team

Requirements:

• You've shipped at least one ML or AI feature that customers use, and lived with it afterward. Recommendations, embeddings, search, LLM-powered features, or similar
• You've been on the hook when a pipeline broke. You investigate with evidence (lineage, logs, data samples) rather than guesses, and you understand ELT/ETL, orchestration, backfills, and data quality from the inside out
• Your data work has a visible output in the product. You've built pipelines and models that feed customer-facing features, not just internal tooling or reporting
• You've written and maintained production-level TypeScript codebases and are comfortable with type systems and debugging
• Complex analytical SQL, with an eye on warehouse cost and performance
• You design before coding, write tests, and do code reviews
• You use AI coding tools productively while owning the quality of the output
• You document as part of the work. Your write-ups stand on their own asynchronously
• Familiar with dbt and a cloud warehouse (BigQuery or Snowflake). Deep expertise isn't required on day one
• You have previous remote experience and are comfortable using tools like Slack, Loom, and Linear to communicate as needed
• MongoDB familiarity is a plus
• Optimization and scheduling (Timefold, OR-Tools): Nice to have
• Genuine interest in the problem space matters more than prior experience