Director of Engineering, Lakehouse Platform

ABOUT TETRASCIENCE

Tetra Science is the Scientific Data and AI Cloud purpose-built for biopharma. Our platform — the Tetra Data Platform (TDP) — harmonizes instrument, lab, and enterprise data into a unified scientific lakehouse, enabling AI-driven drug discovery and development at the world's leading pharmaceutical companies. We are mission-driven and technically ambitious, united by the belief that better data infrastructure accelerates science.

Tetra Science 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 Tetra Science, everyone builds. Engineers, managers, and leadership alike. A pure management background without active technical contribution will not succeed here.

• 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 Tetra flows, Semantic services and Data Resources built on the Lakehouse Platform
• Lead the evolution of Tetra Science'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.

• 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.

• 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.

• 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 Tetra Science's broader AI platform strategy from the Lakehouse data infrastructure layer up.

• 10+ years of engineering experience at top tier technology organizations, with at least 4 years focused on data engineering or distributed systems at production scale.
• Demonstrable expertise in Lakehouse technologies:
  Apache Spark, Delta Lake or Apache Iceberg, Databricks or an equivalent distributed compute platform.
• Experience leading Data Engineering teams (6+ engineers) with direct accountability for strategy, execution, operational excellence and people development.
• Strong command of distributed systems, cloud and modern data stack: columnar and open table formats, query execution engines, partitioning strategies, metadata catalogs and decoupled…