Senior Scientific Data Engineer – R&D Platform

Abbott Laboratories; Illinois; On-site; USD 78,000 - 156,000; Full time

Benefits offered by this job

Health benefits

Job summary

Abbott Laboratories is seeking a Senior Scientific Data Engineer to lead end-to-end data solutions for cancer research and diagnostic development. You will design reusable data tools, pipelines, and applications using Python, SQL, and AWS, collaborating with scientists, data scientists, and DevOps partners to turn ambiguous needs into durable capabilities.

This role emphasizes data quality, documentation, and governance across heterogeneous data sources, including genomic, clinical, imaging, and

Qualifications

• Advanced programming skills in Python.
• Strong SQL skills and experience with structured and semi-structured data.
• Track record of building reusable, maintainable software.
• Experience designing data pipelines, APIs, notebooks, or internal tools.
• Hands-on experience using AWS for data processing and analytics.

Responsibilities

• Lead the design and delivery of reusable tools for ingesting, validating, transforming, documenting, discovering, and sharing scientific data.
• Own platform capability areas end to end including design, implementation, adoption, and maintainability.
• Develop solutions using Python and SQL, including data pipelines, APIs, notebooks, and lightweight applications.
• Create self-service workflows for researchers with varying programming experience.
• Mentor engineers and ensure code quality through reviews and documentation.

Skills

Python

SQL

Data engineering

Software design

Git

Education

Bachelor's degree in computer science or related field

Tools

AWS

Spark/PySpark

Job description

Abbott Laboratories is seeking a Senior Scientific Data Engineer to lead end-to-end data solutions for cancer research and diagnostic development. You will design reusable data tools, pipelines, and applications using Python, SQL, and AWS, collaborating with scientists, data scientists, and DevOps partners to turn ambiguous needs into durable capabilities.

This role emphasizes data quality, documentation, and governance across heterogeneous data sources, including genomic, clinical, imaging, and