Sr. Scientific Data Engineer, R&D Data Platform

Abbott (abbottcareers2 board); Illinois; On-site; USD 130,000 - 190,000; Full time

Job summary

Abbott is seeking a Senior Scientific Data Engineer within the Cancer Diagnostics Science Office to design end-to-end data solutions for cancer research and diagnostic development. You will own parts of the research data platform, building reusable tools, pipelines, APIs, notebooks, and lightweight apps to organize, validate, transform, and share complex data.

You will collaborate with scientists, data scientists, bioinformaticians, and DevOps partners, applying strong software engineering

Qualifications

• Experience designing data platforms for research data and analytics.
• Proficient in Python and SQL for data tools and pipelines.
• Familiarity with Spark/PySpark and AWS data services is a plus.

Responsibilities

• Lead the design and delivery of reusable data tools for ingesting and transforming scientific data.
• Own platform capability areas end-to-end, including adoption and maintainability.
• Develop data pipelines, APIs, notebooks, and lightweight apps using Python/SQL.
• Create self-service workflows for researchers with varying programming skills.
• Collaborate with scientists, data scientists, bioinformaticians, and DevOps partners to translate needs into technical roadmaps.

Skills

Python

SQL

Spark

PySpark

AWS

Tools

S3

Athena

Glue

EMR

Lambda

SageMaker

Job description

Position Overview

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 115,000 colleagues serve people in more than 160 countries.

The Science Office within Abbott Cancer Diagnostics is seeking a Senior Scientific Data Engineer to lead the design and delivery of practical data solutions for cancer research and diagnostic development. This position sits at the intersection of software engineering, scientific data, and applied analysis. You will own significant pieces of our research data platform end to end, building reusable tools and workflows that help researchers organize, validate, discover, transform, analyze, and share complex data. These solutions may include Python packages, data pipelines, APIs, notebooks, workflow utilities, and lightweight web applications. This is not a traditional enterprise data warehousing position. The work centers on heterogeneous research data generated across scientific programs, including genomic, clinical, imaging, laboratory, and experimental data. Successful candidates will combine deep technical skills with an understanding of how quantitative research is conducted, and will be comfortable setting technical direction when a problem is still loosely defined. You will work closely with scientists, data scientists, bioinformaticians, software engineers, and R&D DevOps partners, and will often represent the team in cross-functional technical discussions. The ideal candidate is curious, resourceful, and able to turn ambiguous scientific needs into durable, reusable capabilities, while helping other engineers do the same.

Essential Duties and Responsibilities

• Lead the design and delivery of reusable tools and services for ingesting, validating, transforming, documenting, discovering, and sharing scientific data.
• Own one or more platform capability areas end to end, including design, implementation, adoption, operational support, and long-term maintainability.
• Develop maintainable solutions using Python and SQL, including software packages, data pipelines, APIs, notebooks, workflow utilities, and lightweight internal applications.
• Create approachable, self-service workflows that allow researchers with varying levels of programming experience to prepare and share data consistently.
• Partner directly with scientific teams to understand their studies, analytical workflows, data sources, and recurring technical challenges, and translate those needs into a prioritized technical roadmap.
• Establish standards and reusable patterns for organizing and harmonizing data from disparate sources, including consistent structures, terminology, variable definitions, and mappings, and drive their adoption across teams.
• Design automated data-quality and validation frameworks that identify missing, inconsistent, malformed, or unexpected data before it is used in downstream research.
• Improve the documentation, traceability, and discoverability of scientific datasets, including clear descriptions of data content, origin, ownership, processing history, and intended use.
• Evaluate AWS services and features for scientific data and analytical workflows. Translate research requirements into technical recommendations and partner with R&D DevOps teams on architecture, deployment patterns, and operational ownership.
• Develop solutions that use AWS data and analytics capabilities, particularly Amazon S3 and related services such as Athena, Glue, EMR, Lambda, and SageMaker.
• Prototype solutions for individual research programs and lead the work of generalizing successful approaches into reusable platform capabilities.
• Provide technical leadership on designs that span multiple projects or teams: lead design reviews, document trade-offs and decisions, and align approaches with other engineers and technical leads.
• Mentor engineers through code review, pairing, design feedback, and documentation, and raise the overall engineering standard of the team.
• Support hands-on preparation and analysis of scientific data when needed to understand a problem, validate a solution, or accelerate a research effort.
• Apply quantitative and scientific judgment when evaluating data, analytical requirements, and potential technical solutions.
• Use Spark or PySpark when distributed processing is appropriate for large or computationally intensive datasets.
• Apply and reinforce sound software-engineering practices, including version control, testing, code review, documentation, dependency management, continuous integration, and reproducible development.
• Communicate technical concepts, design decisions, trade-offs, limitations, and project status clearly to technical, scientific, and leadership audiences.
• Operate independently within an evolving environment: scope amb