Sr. Biometrics Data Engineer

About This Role

Act as a hands-on technical lead to design, code, deploy, and maintain scalable ETL pipelines and data structures, specifically focusing on the ingestion and transformation of complex datasets (genomics, proteomics, imaging, lab data) into modern cloud architectures within the biopharmaceutical solutions organization.

Responsibilities

• Act as a hands-on technical lead who not only defines the architecture but also codes, deploys, and maintains scalable ETL pipelines and data structures.
• Spearhead the technical implementation of the Translational Data Lake data ingestion, managing the ingestion of complex datasets into modern cloud architectures.
• Lead data engineering projects beyond the Data Lake, designing bespoke integration solutions for diverse scientific data sources across the Research organization.
• Design and script automated procedures to normalize unformatted data from external vendors (CROs) into a structured Common Data Model (CDM).
• Partner with various functions in Research and IT to align infrastructure with scientific needs, ensuring solutions are robust, FAIR-compliant, and scalable.
• Develop and communicate the technical vision for biomarker data integration and reuse.
• Architect and implement scalable ETL procedures, APIs and front-end tools for data access and visualization.
• Engage stakeholders to gather requirements and incorporate feedback into design.
• Lead user acceptance testing (UAT) and ensure high-quality deliverables.
• Collaborate with IT and Translational leads to align infrastructure and governance processes.
• Champion FAIR principles and interoperability across translational and clinical programs.

Requirements

• 8+ years of professional experience in data engineering or software architecture, with a focus on building production-grade data pipelines
• Expert-level coding proficiency in Python with mastery of modern data engineering libraries (Pandas, PySpark, Dask, SQLAlchemy)
• Advanced proficiency with SQL, workflow orchestration tools (Airflow, Dagster, or Prefect), and containerization (Docker/Kubernetes)
• Deep experience with modern Data Lake and Lakehouse architectures (e.g., Azure Fabric, Databricks, Snowflake), with a proven track record of connecting and integrating disparate data sources
• Solid understanding of data modeling, ETL processes, and schema design for complex datasets
• Experience designing and deploying APIs for data access
• Excellent communication skills to bridge the gap between IT infrastructure and scientific stakeholders
• Familiarity with FAIR principles and metadata standards for scientific data

Qualifications

• Bachelor’s or master’s degree in computer science, Data Engineering, Bioinformatics, or related field
• 8+ years of professional experience in data engineering or software architecture, with a focus on building production-grade data pipelines

Nice to Have

• Familiarity with clinical data standards including SDTM, ADaM, and CDISC, and biomarker data formats (NGS variant results, flow cytometry, serum proteomics, gene expression profiling)
• Direct experience with Azure Fabric tools for connecting and integrating data sources
• Proficiency in R for interoperability with bioinformatics teams

Skills

* Required skills

Benefits