Remote Senior Scientific Data Engineer for Cancer Research

Abbott; Chicago (IL); Remote; USD 78,000 - 156,000; Full time

Job summary

Abbott, a global healthcare leader, seeks a Senior Scientific Data Engineer to design and deliver practical data solutions for cancer research and diagnostics. You will own substantial parts of the research data platform, building reusable tools, pipelines, and lightweight apps to organize, validate, transform, analyze, and share complex data.

You will work across scientists, data scientists, bioinformaticians, and DevOps partners to turn ambiguous needs into durable capabilities, mentoring

Qualifications

• Bachelor's degree in a quantitative field.
• 5+ years of relevant professional or applied research experience, or 3+ years with an advanced degree.
• Advanced programming skills in Python.
• Strong SQL skills and experience with structured data.
• Demonstrated track record building reusable, maintainable software.
• Experience designing data pipelines, APIs, notebooks, or internal tools.
• Hands-on experience using AWS for data processing and analytics.
• Experience with Git, automated testing, and continuous integration.

Responsibilities

• Lead the design and delivery of reusable tools and services for scientific data.
• Own platform capability areas end to end including design, adoption, and maintainability.
• Develop solutions using Python and SQL, including pipelines and APIs.
• Create self-service workflows for researchers with varying programming experience.
• Partner with scientific teams to translate studies into technical roadmaps.
• Establish standards for organizing data from disparate sources and drive adoption.

Skills

Python

SQL

AWS

Data pipelines

Software design

Education

BS in quantitative field

Tools

Spark

Job description

Abbott, a global healthcare leader, seeks a Senior Scientific Data Engineer to design and deliver practical data solutions for cancer research and diagnostics. You will own substantial parts of the research data platform, building reusable tools, pipelines, and lightweight apps to organize, validate, transform, analyze, and share complex data.

You will work across scientists, data scientists, bioinformaticians, and DevOps partners to turn ambiguous needs into durable capabilities, mentoring