Senior Data Engineer

Float•New York, NY

15d•Remote

About The Position

Float's Intelligence team is building the AI layer that makes resource management smarter, offering 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 achieve this, we need production-grade and ML-ready data pipelines. The Senior Data Engineer will build and own these pipelines, maintain the data warehouse, and contribute to an evolving, event-driven architecture. This role involves working asynchronously with a global team, focusing on creating simple and intuitive solutions for complex problems.

Requirements

Shipped at least one ML or AI feature that customers use and have experience maintaining it (e.g., recommendations, embeddings, search, LLM-powered features).
Experience with pipeline ownership and debugging, investigating issues with evidence (lineage, logs, data samples), and understanding ELT/ETL, orchestration, backfills, and data quality.
Experience building pipelines and models that feed customer-facing features.
Written and maintained production-level TypeScript codebases, comfortable with type systems and debugging.
Proficiency in complex analytical SQL, with an understanding of warehouse cost and performance.
Experience with software engineering practices: designing before coding, writing tests, and conducting code reviews.
Ability to use AI coding tools productively while owning the quality of the output.
Experience with documentation and written communication for asynchronous work.
Familiarity with dbt and a cloud warehouse (BigQuery or Snowflake).
Comfortable with asynchronous communication and previous remote experience.
Comfortable using tools like Slack, Loom, and Linear.

Nice To Haves

MongoDB familiarity.
Optimization and scheduling experience (Timefold, OR-Tools).
Genuine interest in the problem space of optimization and scheduling.

Responsibilities

Take end-to-end ownership of the data pipelines that power NextGen and other Intelligence team features.
Contribute to AI features in production by building the data infrastructure that makes those features work reliably.
Ensure pipelines and data infrastructure are reliable and well-documented, working alongside the Data team.
Play a key role in introducing event streaming (Kafka/Debezium) into the stack, contributing to architectural decisions and implementation.
Develop familiarity with Timefold and Float's scheduling domain.
Improve workflows beyond the Intelligence team through pipeline and infrastructure enhancements.