Senior Data Engineer

Float.com; New York (NY); Remote; USD 149,000 - 201,000; Full time

Job summary

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.

As a Staff Software Engineer (Architecture) you will own end-to-end data pipelines, shape an event-driven stack, and collaborate with a global, async-first team to deliver reliable ML-enabled features for customers.

Qualifications

• Applied ML/AI in production: shipped at least one ML or AI feature used by customers.
• Pipeline ownership and debugging: evidence-based troubleshooting, ETL/ELT, orchestration, backfills, data quality.
• Feature delivery: data work feeds customer-facing features, not only internal tooling.
• TypeScript/Node.js: production-level TS codebases and debugging.
• SQL and databases: complex analytical SQL with cost/performance awareness; MongoDB familiarity a plus.
• Software engineering practices: design before coding, tests, and code reviews.
• Documentation and written communication: written outputs stand alone asynchronously.
• Warehouse modeling: dbt and cloud warehouses such as BigQuery or Snowflake.
• Optimization and scheduling: Timefold and OR-Tools interest or experience.

Responsibilities

• Intelligence pipeline ownership: end-to-end ownership of data pipelines powering features.
• Applied ML in production: contribute to AI features and reliable data infra.
• Pipeline health and maintenance: ensure pipelines are reliable and well-documented.
• Event-driven architecture: contribute to streaming with Kafka/Debezium and architectural decisions.
• Scheduling optimization work: develop familiarity with Timefold and Float scheduling domain.
• Cross-team data impact: improvements ripple to other workflows over time.

Skills

Applied ML/AI in production

Pipeline ownership

Feature delivery

TypeScript/Node.js

SQL and databases

Software engineering practices

Documentation and communication

Warehouse modeling

Optimization and scheduling

Tools

Timefold

OR-Tools

Kafka

Debezium

dbt

BigQuery

Snowflake

MongoDB

Job description

Description

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.

Department

Engineering

Compensation

$174,893 / year

What you’ll be responsible for

• 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.

What you’ll need to be successful

• 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.
• TypeScript/Node.js: You’ve written and maintained production-level TypeScript 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 (BigQuery or Snowflake). Deep expertise isn’t required on day one.
• Optimization and scheduling (Timefold, OR-Tools): Nice to have. Genuine interest in the problem space matters more than prior experience.

As a fully remote team, we’re looking for someone comfortable with asynchronous communication as the default, which means you have previous remote experience and are comfortable using tools like Slack, Loom, and Linear to communicate as needed. Don’t worry—you will have significant deep work time since we have very few meetings.

Note: Industry research shows that women and those in traditionally underrepresented groups are less likely to apply to jobs unless they check all the boxes for the role. If you feel strongly that you have what it takes for this role but don’t check 100% of the boxes, that’s okay! We encourage you to apply anyway and highlight what you can bring to the table.

Why join us

Pay for this role is US $174,893 (Level 3). Here’s a blog post with more information on how we determine our salaries.

We’re a global async remote company with a diverse team of people from all over the world who share a common belief in living our best work life. We believe deeply in the idea of transparency and share our Float Handbook publicly, so potential new team members can get familiar with our perks & benefits as well as our ways of working. If you feel like you can thrive at Float to do your best work, we would love to hear from you.