Sr. Scientific Data Engineer, R&D Data Platform

Overview In this role you will lead the design and delivery of reusable data tools for cancer research at Abbott. You build end-to-end data platform capabilities that empower researchers to organize, validate, transform, and share complex data. You work across scientists, data scientists, bioinformaticians, and engineers to turn ambiguous needs into durable, scalable solutions. You shape how scientific data is captured, validated, and made discoverable, contributing to faster, data-driven discoveries in cancer diagnostics. ResponsibilitiesLead the design and delivery of reusable tools for ingesting, validating, transforming, documenting, discovering, and sharing scientific dataOwn platform capability areas end to end, including design, adoption, and maintainabilityDevelop maintainable solutions using Python and SQL (packages, pipelines, APIs, notebooks, tools)Create self-service workflows for researchers to prepare and share data consistentlyCollaborate with scientific teams to translate studies and workflows into a technical roadmapEstablish standards for harmonizing data from diverse sources and promote adoptionDesign automated data-quality checks to catch issues before downstream useImprove dataset documentation, traceability, and discoverabilityEvaluate AWS services for scientific data and partner with DevOps on architecture and deploymentDevelop AWS-based solutions (S3, Athena, Glue, EMR, Lambda, SageMaker) and generalize into reusable platform capabilitiesProvide technical leadership on cross-project designs, reviews, and trade-offsMentor engineers through review, pairing, and documentationSupport hands-on data preparation and analysis when neededApply quantitative judgment to evaluate data and technical solutionsUse Spark or PySpark for distributed processing when appropriateUphold software-engineering practices: version control, testing, CI, documentationCommunicate complex concepts clearly to technical, scientific, and leadership audiencesOperate independently in an evolving environment and keep stakeholders informed Key requirementsBachelor’s degree in a quantitative field or equivalentFive+ years of relevant experience, or three+ years with advanced degreeAdvanced programming skills in PythonStrong SQL skills with structured and semi-structured dataTrack record of building reusable, maintainable softwareExperience designing data pipelines, Python packages, APIs, analytics workflows, notebooks, or internal toolsHands-on AWS experience for data processing and analyticsDepth in AWS architecture to evaluate options and define infrastructureExperience in quantitative research, statistical analysis, ML, or data-intensive workExperience cleaning and integrating data from multiple sources at scaleFluency with Git, testing, documentation, code reviews, and CIAbility to work with ambiguous problems and deliver working solutionsExperience mentoring or guiding other engineers or scientistsStrong communication and collaboration skillscollaborationcommunicationmentoringPythonSQLAWS