Leads Scientific Data Engineering team to build data pipelines, schemas, and parsers for pre-clinical lab data using Python/SQL/AI. Architects solutions, mentors juniors, and delivers customer-focused data products with dashboards in React/Streamlit. Requires 8+ years experience.
Salary not listed
On-site8+ YOEData Engineering
About the role
Responsibilities
Lead the Scientific Data Engineering (SDE) Team and build Tetra Data and productizable solutions.
Work with Product Managers and Solution Architects to understand business requirements and build solutions.
Take ownership of building data models, prototypes, and integration solutions.
Use AI agents to build comprehensive data schemas and parsers for pre-clinical data (R&D lab instruments, manufacturing, CRO, CDMO, ELN, LIMS) with formats: .xlsx, .pdf, .txt, .raw, .fid, vendor binaries.
Extract reusable schema components and parsing functions, productize into Python libraries.
Build high-quality data pipelines with full unit test and integration test coverage.
Build data applications, reports, and dashboards using React, Streamlit, Jupyter notebook.
Collaborate with product managers, project managers, business analysts, data architects, and ML engineers.
Drive customer value, verify solutions, and act as quality gatekeeper.
Lead team-wide process/technology improvements and Agile Sprint commitments.
Provide mentorship to junior SDEs.
Requirements
8+ years as Data Engineer or similar.
8+ years in Python and SQL focused on data.
Experience leading projects, managing requirements, timelines, and cross-functional customer implementations.
Experience with data plotting/dashboarding tools like React and/or Streamlit (strongly preferred).
Experience with pre-clinical data and lab scientists (strongly preferred).
Excellent communication, attention to detail, and project delivery leadership.
Benefits
100% employer paid benefits for employees and immediate family.
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