Keebo automatically optimizes cloud data warehouses for Snowflake and Databricks to control compute costs without hurting query performance. Our platform learns each customer's workloads and adjusts warehouse settings in real time, so data teams don't have to. We're venture-backed, built on years of database and machine learning research, and fully remote.
About the Opportunity
We're an AI-first engineering team. We use agents across design, implementation, review, and testing, and we hold their output to the same standard as any other pull request: read it, question it, and reject it when it's wrong. We adopt new tools and processes quickly, and we drop what isn't working.
As a Senior Data Engineer, you'll design and scale the data pipelines and machine learning infrastructure behind our platform. You'll use AI to multiply your own output and the team's, and work directly with product and machine learning engineers to turn customer data into models and features that ship.
You Will
Design and build reliable data pipelines that process large volumes of customer warehouse data
Build and run the machine learning operations infrastructure that takes models from training to production: feature pipelines, training jobs, deployment, and monitoring
Own data quality and data modeling, so the data behind our models and customer-facing features is accurate and trustworthy
Use AI coding tools effectively by writing clear specs and context, running agents on parallel workstreams, and reviewing their output critically
Build shared AI workflows, guardrails, and review standards that make everyone move faster
Evaluate new tools and models, and share the worthy ones with the team
Write architecture docs that explain what we're building, the tradeoffs, and why
Understand customer needs and decide what's worth building
Mentor engineers, including on working effectively with AI coding tools
You Have
5+ years of data engineering or software engineering experience, including time at an early-stage startup (Seed through Series B)
Deep experience building and scaling production data pipelines, batch and streaming. Particularly with orchestration tools such as Dagster, Airflow, or Temporal
Hands-on machine learning operations experience: deploying, monitoring, and retraining models in production
Strong data modeling and architecture skills, and a track record of keeping data quality high as volume grows
Deep experience with AI coding agents, and examples of how to scale their use across a team
The judgment to catch AI output that is wrong, incomplete, or risky, especially with customer data, and the habit of adapting as tools change
Sound tradeoffs: you know when to ship something simple and when reliability, data integrity, or architecture can't wait
Proven expertise in SQL and Python
Experience with cloud platforms, workflow orchestration, containers, and automated deploy pipelines
Clear, collaborative communication, especially in writing: you explain what matters and why
Persistence and a constructive attitude when things break or plans change
Bonus Points
Experience with data cloud platforms, especially Snowflake or Databricks
Hands-on experience applying machine learning techniques such as time-series forecasting, regression, anomaly detection, or reinforcement learning
Experience building AI agents, agent tooling, or Model Context Protocol servers
Knowledge of query optimization or workload analysis
Working knowledge of Java, Go, or Javascript
Compensation and Location
The base salary is $170,000 to $200,000 for US-based candidates, plus equity. The role is fully remote and open to candidates living in the United States with authorization to work there. Visa support will not be provided. Core hours align with US Eastern Time.
What we Offer
Working with a world-class team of researchers and engineers in machine learning to turn Al algorithms into scalable and automated cloud products
For full-time positions:
Equity
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