Director of Engineering (Lakehouse Platform)

Join TetraScience, a leading company in scientific data analysis and AI products. As the Director of Engineering, you will lead the Lakehouse Data Products Platform team, owning the architecture and driving technical and operational strategy. This is a hands-on role where you will develop internal tools and systems alongside your team. You will also lead, manage, and grow a team of 8+ engineers.

• Architect and evolve the Lakehouse Platform and Infrastructure foundations, including storage layer, catalog, Spark query engine, and platform data pipelines.
• Lead technical prioritization and delivery across a team of 8+ engineers, driving sprint-level execution and quarterly delivery commitments.
• Coach engineers at all levels, providing technical mentorship, growth plans, and direct performance feedback, while also hiring and developing senior individual contributors and tech leads.
• 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
• Clear written and verbal communication; can present architectural decisions to both technical and non-technical stakeholders
• 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
• Cloud-native fluency: AWS, GCP, or Azure, containerization, and Infrastructure-as-Code (Terraform or equivalent)
• Track record of building, shipping and operating Tier 1 production data platforms and products
• 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
• 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