Senior Scientific Data Engineer

TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. The company helps leading life sciences firms transform fragmented scientific data into AI-native assets and scientific workflows to accelerate discovery, development, and manufacturing. TetraScience has strategic partners including NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft.

• Proficiency in building data products as a Data Engineer or similar role.
• Proficiency in Python and SQL focused on data, integrations, and scientific applications.
• Expertise in Python and working knowledge of JavaScript, TypeScript, Node, and React.
• Experience leading customer-facing implementation projects across cross-functional teams.
• Experience working with non-clinical data and lab scientists.
• Familiarity with biotech and pharmaceutical industries and regulated environments such as GxP.
• Knowledge of networking, CI/CD, and full stack development.
• Experience with cloud engineering on AWS, including services like S3, Lambda, ECS, SQS, SNS, EC2, and CloudWatch.
• Excellent communication skills, attention to detail, and confidence in project delivery management.
• Ability to quickly understand technical products and communicate effectively with multiple stakeholders.

• Act as a forward deployed engineer for a portfolio of biotech, pharmaceutical, and scientific customers, owning the technical relationship and delivering solutions aligned with their scientific workflows.
• Design and build bidirectional integrations between Tetra OS and customer systems such as ELN, LIMS, MES, and process historians using serverless push and pull pipelines, stateful connectors, and full stack data applications in Python and TypeScript.
• Develop comprehensive data schemas and parsers for scientific data from R&D lab instruments, manufacturing, CRO, CDMO, ELN, and LIMS sources across various formats.
• Deploy and test solutions in customer environments including cloud, on-premises, and hybrid networks and troubleshoot full stack issues.
• Collaborate with product managers, solution architects, business analysts, data architects, and ML engineers to understand requirements and build data products.
• Create and enforce designs backed by comprehensive test automation.
• Lead process and technology improvements to enhance product quality, developer experience, and platform capabilities.
• Facilitate Agile sprint commitments, identify team inefficiencies, and resolve blockers.
• Provide mentorship and leadership to junior scientific data engineers.