Sr. Biometrics Data Engineer

About This Role

This role involves acting as a hands-on technical leader, architecting and implementing scalable ETL pipelines and data structures, and spearheading the technical implementation and broader integration of the Translational Data Lake for various scientific data sources.

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 (genomics, proteomics, imaging, lab data, etc.) 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.

Requirements

• 8+ years of professional experience in data engineering or software architecture
• Expert-level coding proficiency in Python with specific 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.
• Excellent communication and collaboration skills.

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