Keebo are hiring a Senior Data Engineer

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.