Senior Data Engineer

Department: Engineering

Employment Type: Permanent - Full Time

Location: Remote (global)

Reporting To: Davi Candido

Compensation: $174,893 / year

Float's Intelligence team is building the AI layer that makes resource management smarter: recommendations that reflect how your team actually works, scheduling that optimizes for real constraints, and insights that surface before it's too late to act.

To get there, we need data pipelines that are production-grade and ML-ready. You'll be the engineer who builds and owns the pipelines that Intelligence features depend on, keeps the warehouse healthy and well-understood, and grows alongside us as we move toward a more event-driven architecture.

Our Staff Software Engineer (Architecture), Roberto, explains the important role you will play within our Data team. Watch this video!

You’ll be working asynchronously with a bright, dedicated team from across the globe, with a strong focus on taking complex problems and creating solutions that feel simple and intuitive for our customers.

Early on, you’ll jump right into:

• Intelligence pipeline ownership: You'll take end-to-end ownership of the data pipelines that power Next Gen 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.

Once you are a bit more settled, we expect that you will jump into the following projects:

• 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.

We want you to love your work and believe that these skills will allow you to succeed in the role. Applying these skills requires:

• Applied ML/AI in production: 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.
• Pipeline ownership and debugging: 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.
• Feature delivery: 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.
• Type Script/Node.js: You’ve written and maintained production-level Type Script codebases and are comfortable with type systems and debugging.
• SQL and databases: Complex analytical SQL, with an eye on warehouse cost and performance. MongoDB familiarity is a plus.
• Software engineering practices: You design before coding, write tests, and do code reviews. You use AI coding tools productively while owning the quality of the output.
• Documentation and written communication: You document as part of the work. Your write-ups stand on their own asynchronously.
• Warehouse modeling: Familiar with dbt and a cloud warehouse (Big Query or Snowflake). Deep expertise isn’t required on day one.
• Optimization and scheduling (Timefold, OR-Tools): Nice to have. Genuine…