Lead Scientific Data Platform Engineer (R&D)

Abbott Laboratories; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Abbott Laboratories' Science Office within Cancer Diagnostics seeks a Senior Scientific Data Engineer to lead end-to-end data solutions for cancer research and diagnostics. You will build reusable tools, pipelines, APIs, notebooks, and lightweight apps to organize, validate, transform, analyze, and share complex data.

You will partner with scientists and engineers, design scalable AWS-based platforms, mentor teammates, and drive data-quality standards across programs.

Qualifications

• Bachelor's degree in a quantitative field.
• Five or more years of relevant professional or three+ years with an advanced degree.
• Advanced programming skills in Python.
• Strong SQL skills with structured and semi-structured data.
• Track record of building reusable, maintainable software.
• Experience with data pipelines, Python packages, APIs, notebooks, or internal tools.
• Hands-on experience using AWS for data processing.
• Depth in AWS services and architecture to evaluate options.
• Experience with quantitative research such as statistics or ML.
• Experience cleaning/integrating data from multiple sources at scale.
• Fluency with Git, automated testing, documentation, code review, and CI.
• Ability to investigate ambiguous problems and deliver working solutions.

Responsibilities

• Lead design and delivery of reusable tools and services for data ingestion, validation, transformation, documentation, discovery, and sharing.
• Own one or more platform capability areas end to end, including design, implementation, adoption, and maintenance.
• Develop maintainable solutions using Python and SQL, including packages, pipelines, APIs, notebooks, and lightweight apps.
• Create self-service workflows for researchers with varying programming experience.
• Partner with scientific teams to map studies to a technical roadmap.
• Establish standards for organizing and harmonizing data from disparate sources.
• Design automated data-quality and validation frameworks.
• Improve dataset documentation, traceability, and discoverability.
• Evaluate AWS services and translate requirements into architecture with DevOps partners.
• Develop solutions using AWS data capabilities (S3, Athena, Glue, EMR, Lambda, SageMaker).
• Prototype solutions for programs and generalize into reusable platform capabilities.
• Provide technical leadership across projects: design reviews and trade-offs.
• Mentor engineers through code review and pairing, raising standards.
• Support hands-on data analysis when needed to accelerate research.
• Apply quantitative judgment to data and analytical needs.
• Use Spark or PySpark for distributed processing when appropriate.
• Adhere to software engineering practices: version control, testing, documentation, CI.
• Communicate technical concepts clearly to technical and leadership audiences.
• Operate independently in evolving environments with ambiguous requirements.

Skills

Python programming

SQL

Data analysis

Software design

Git & CI

Education

Bachelor’s degree in a quantitative field

Tools

Spark / PySpark

AWS (S3, Glue, Athena, EMR, Lambda, SageMaker)

APIs / notebooks

Job description

Abbott Laboratories' Science Office within Cancer Diagnostics seeks a Senior Scientific Data Engineer to lead end-to-end data solutions for cancer research and diagnostics. You will build reusable tools, pipelines, APIs, notebooks, and lightweight apps to organize, validate, transform, analyze, and share complex data.

You will partner with scientists and engineers, design scalable AWS-based platforms, mentor teammates, and drive data-quality standards across programs.