Director Of Engineering, Lakehouse Platform

Director Of Engineering

TetraScience is hiring a Director of Engineering to lead the Lakehouse Data Products Platform team. The Lakehouse Platform is the foundational data infrastructure and platform layer that powers all scientific data analysis and AI products on Tetra OS.

You will own the architecture, drive technical and operational strategy aligned with product and customer growth. As an AI-forward, hands-on leader, you will develop internal tools and systems alongside your team. As a deeply technical domain expert, you will lead, manage and grow a team of 8+ engineers spanning Data Platforms and Infrastructure engineering.

This is a player-coach role. You will be deeply fluent in the modern data and AI stack and build internal tools, lead architecture and code reviews. At TetraScience, everyone builds. Engineers, managers, and leadership alike. A pure management background without active technical contribution will not succeed here.

Architecture and Technical Strategy

• Own and evolve the Lakehouse Platform and Infrastructure foundations: Storage layer, catalog, Spark query engine, Databricks IaC, Platform Data Pipelines and releases.
• Lakehouse Developer Platform and DX that supports Tetraflows, Semantic services and Data Resources built on the Lakehouse Platform
• Lead the evolution of TetraScience's Lakehouse architecture from first principles across open table formats, partition strategies, schema evolution, governance, and API contracts.
• Design for durability: version coupling, artifact deployment safety, and protocol compatibility across major platform releases.
• Collaborate with other platform and infrastructure teams to define integration contracts, data pipelines, shared execution roadmaps and operational excellence standards.

Engineering Execution

• Own technical prioritization and delivery across a team of 8+ engineers; drive sprint-level execution and quarterly delivery commitments.
• Lead incident response and root-cause analysis for production issues; build systemic fixes, not one-off patches.
• Make and defend build-vs-buy decisions for Lakehouse components based on strategic fit and engineering cost.
• Establish and maintain engineering standards: testing practices, observability instrumentation, and release safety specific to data infrastructure.

People and Team Development

• Coach engineers at all levels — technical mentorship, growth plans, and direct performance feedback delivered consistently.
• Hire and develop senior ICs and tech leads; build team depth to reduce knowledge concentration risk.
• Model the builder culture: write code, ship internal tools, and set the bar for technical craft on your team.

AI-Forward Development

• Champion AI-assisted development practices across the team — your own workflow should demonstrate what good looks like.
• Identify opportunities to apply AI to data quality, schema inference, anomaly detection, and platform observability on the Lakehouse layer.
• Contribute to TetraScience's broader AI platform strategy from the Lakehouse data infrastructure layer up.