ABOUT TETRASCIENCE

TetraScience is the Scientific Data and AI Cloud purpose-built for biopharma. Our platform — Tetra OS — 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.

THE ROLE

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 with develop internal tools and systems alongside your team. As a deeply technical domain expert, you with lead, manage and grow a team 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.

WHAT YOU WILL DO

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.

Requirements

WHAT YOU WILL BRING

Required

• 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 storage and compute platforms
• Track record of building, shipping and operating Tier 1 production data platforms and products.
• Cloud-native fluency: AWS, GCP, or Azure, containerization, and Infrastructure-as-Code (Terraform or equivalent).
• Clear written and verbal communication; can present architectural decisions to both technical and non-technical stakeholders.

Strong Advantage

• Experience in regulated industries (life sciences, healthcare, financial services) with data governance and auditability requirements.
• Familiarity with scientific data pipelines, analysis or instrument data pipelines.
• Real-time or near-real-time streaming experience (Kafka, Apache Flink, or equivalent).
• Exposure to AI/ML workflows over Lakehouse data: feature stores, ML pipelines, or vector search infrastructure.
• Prior career arc as a senior IC (Staff+ / Principal Engineer equivalent) before moving into engineering leadership.

WHY TETRASCIENCE

• Mission-driven: your infrastructure directly enables scientific drug development processes that save and extend lives.
• Builder culture: everyone ships — engineers, managers, and leadership. No passengers.
• Technically ambitious: we are rebuilding how science-grade data infrastructure works, not deploying off-the-shelf solutions.
• AI-first: we apply AI across every layer of the platform today, not as a future roadmap item.
• Competitive compensation, meaningful equity, and strong benefits.

Benefits

• ​Competitive compensation
• Stock options in a VC-backed Series C company
• 100% employer-paid benefits for all eligible employees and immediate family members
• 401(k)
• Unlimited paid time off (PTO)
• Flexible working arrangements
• Company-paid Life Insurance, LTD/STD

The salary range for this position is $180,000-$270,000. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity.