Data Engineer

Fluency is enabling the autonomous Enterprise.

You're needed to help pioneer a new software category that will change how enterprises work. Welcome to the data layer of the future.

Fluency is looking for a next-level data engineer to build the data infrastructure the ontologizes how work happens across Fortune 500 organizations.

You'll be working on problems that have no established solutions . We're processing terabytes of workflow data per enterprise customer in real-time - systems that map, relationship, contextualize, and analyze work patterns across entire orgs. Think human genome project, but for enterprise workflows.

You'll invent new data primitives and paradigms. This means designing novel data structures for representing work, building pipelines that process billions of events per day, and writing whitepapers on approaches that don't exist yet.

You're operating at the bleeding edge of data science where the playbook hasn't been written.

We're backed by T1 VCs like Accel and are hitting an inflection point with Enterprises all around the globe.

You'll work directly with founders and our engineering team. The technical challenges are unprecedented : real-time processing at massive scale, creating a universal taxonomy for work across every industry, and building systems that can handle the most complex enterprise data environments on earth.

About the Role

We're looking for someone with :

Strong programming knowledge, Python preferred

Software engineering fundamentals - APIs, backend services

SQL + DBT (or SQLMesh) proficiecy

Infrastructure as code : Terraform (or equivalent)

Orchestration : Dagster / Airflow / Prefect

Production postgres experience

Datalake experience (Iceberg, Databricks, etc)

Computer Science Background - with caveat.

• If you don't have a CS background, you're challenged to beat one of the founders in a 1 : 1 whiteboard duel on DS&A judged by Hung. Neither founders have formal CS background, but come prepped.

There will be an expectation to stay up to business context, which could involve :

Watching key customer calls

Interacting with customers!

Helping with product thinking

Strongly Preferred

Has trained models before (even personal projects count, perhaps especially)

You have interesting personal projects

Pytorch or similar framework experience

Kubernetes

Ray for distributed compute

Understand ML / DL systems from an infra perspective

Deep learning theory understanding

Esoteric knowledge

Our Customers

We work with some of the world’s largest :

Financial services enterprises (Aon)

Manufacturing enterprises (Misumi)

And many more across the enterprise spectrum (PVH)

Our Culture

You're expected to be in love with the craft. You're expected to like laughing. You're expected to want to work on novel problems. You're expected to find satisfaction in novelty. You're expected to solve under obscurity.

Our Values

In hesitation lies destruction; in action, glory.

Those who merely meet expectations abandon the pursuit of greatness.

One who dwells within the forum must regard it as hallowed ground.

One who has not tasted the grapes declares them sour.

One who sits alone at the feast misses the richness of the table.

Location

Full-time, in-person role based in San Francisco, CA.

We offer E3 sponsorship for Australians to relocate with stipend

Compensation

US$150K - $250K salary, depending on candidate and experience

Substantial equity - every offer includes ownership

Mac, Linux, or Windows - your call

High-impact work with global enterprises

Technical, product-led founders

Don't apply if :

You want hybrid or remote

You don't like working hard and with insane velocity

You want to work a 9 ->

You’re not comfortable with rapid iteration

You prefer working on CRUD apps

You don’t have personal projects

You dislike AI

You aren’t ambitious

Hiring Process

Resume screen

1 : 1 with founder

Interview w / engineering team

Work through something with the team

Offer

We strongly encourage applicants from underrepresented backgrounds to apply. Diverse teams build better products — see value #5.