{"id":132547,"url":"https://alion.io/job/abbott-sr-scientific-data-engineer-rd-data-platform","title":"Sr. Scientific Data Engineer, R&D Data Platform","company":{"id":2710,"name":"Abbott","domain":"abbott.com","url":"https://alion.io/company/abbott","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":91,"open_postings":269,"ghost_share":0.004,"stale_share":0.539,"repost_share":0.033,"time_to_fill_p50_days":14,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":78000,"max":156000,"currency":"USD","period":"year","gross":null,"usd_annual":156000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"AWS Glue","optional":true},{"name":"AWS Step Functions","optional":true},{"name":"Rest API","optional":true}],"status":"live","first_seen_at":"2026-08-26T00:00:00Z","employer_posted_date":"2026-08-26","last_verified_at":"2026-10-22T15:06:15Z","board_verified":true,"closed_at":null,"days_open":57,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":57},"description":"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.JOB DESCRIPTION:\nPosition Overview:\nThe 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.\nEssential Duties and Responsibilities:\nLead the design and delivery of reusable tools and services for ingesting, validating, transforming, documenting, discovering, and sharing scientific data.\n\nOwn one or more platform capability areas end to end, including design, implementation, adoption, operational support, and long-term maintainability.\n\nDevelop maintainable solutions using Python and SQL, including software packages, data pipelines, APIs, notebooks, workflow utilities, and lightweight internal applications.\n\nCreate approachable, self-service workflows that allow researchers with varying levels of programming experience to prepare and share data consistently.\n\nPartner 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.\n\nEstablish 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.\n\nDesign automated data-quality and validation frameworks that identify missing, inconsistent, malformed, or unexpected data before it is used in downstream research.\n\nImprove the documentation, traceability, and discoverability of scientific datasets, including clear descriptions of data content, origin, ownership, processing history, and intended use.\n\nEvaluate 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.\n\nDevelop solutions that use AWS data and analytics capabilities, particularly Amazon S3 and related services such as Athena, Glue, EMR, Lambda, and SageMaker.\n\nPrototype solutions for individual research programs and lead the work of generalizing successful approaches into reusable platform capabilities.\n\nProvide 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.\n\nMentor engineers through code review, pairing, design feedback, and documentation, and raise the overall engineering standard of the team.\n\nSupport hands-on preparation and analysis of scientific data when needed to understand a problem, validate a solution, or accelerate a research effort.\n\nApply quantitative and scientific judgment when evaluating data, analytical requirements, and potential technical solutions.\n\nUse Spark or PySpark when distributed processing is appropriate for large or computationally intensive datasets.\n\nApply and reinforce sound software-engineering practices, including version control, testing, code review, documentation, dependency management, continuous integration, and reproducible development.\n\nCommunicate technical concepts, design decisions, trade-offs, limitations, and project status clearly to technical, scientific, and leadership audiences.\n\nOperate independently within an evolving environment: scope ambiguous problems, sequence the work, make defensible decisions when requirements are incomplete, and keep stakeholders informed.\n\nMinimum Qualifications:\nBachelor’s degree in computer science, data science, engineering, statistics, mathematics, bioinformatics, computational science, or another relevant quantitative discipline.\nFive or more years of relevant professional or applied research experience, or three or more years with an advanced degree in a relevant field.\nAdvanced programming skills in Python.\nStrong SQL skills and experience working with structured and semi-structured data.\nDemonstrated track record of building reusable, maintainable software that others depend on, rather than one-time scripts or analyses.\nExperience designing and delivering several of the following: data pipelines, Python packages, APIs, analytical workflows, notebooks, or internal software tools.\nSubstantial hands-on experience using AWS for data processing, analytics, scientific computing, or software development.\nSufficient depth in AWS services and architecture to evaluate technical options, justify design recommendations, and define infrastructure requirements with DevOps or cloud-engineering partners.\nExperience conducting or supporting quantitative research, such as statistical analysis, machine learning, computational modeling, or another data-intensive research activity.\nExperience cleaning, integrating, standardizing, or validating data from multiple sources at meaningful scale.\nFluency with software-development practices such as Git, automated testing, technical documentation, code review, and continuous integration.\nDemonstrated ability to investigate ambiguous problems, define an approach, and deliver a working solution with little guidance.\nExperience mentoring or providing technical guidance to other engineers, scientists, or analysts.\nStrong communication and collaboration skills, particularly when building alignment across scientific and technical disciplines.\nPreferred Qualifications:\nAdvanced degree in a quantitative, computational, or life-science discipline.\nExperience working with biomedical, genomic, clinical, proteomic, imaging, laboratory, or other complex scientific data.\nExperience supporting research in life sciences, healthcare, diagnostics, or a similarly data-intensive and regulated scientific environment.\nProduction experience with Spark or PySpark and distributed data processing.\nDepth in AWS services such as Athena, Glue, EMR, SageMaker, Lambda, Step Functions, Lake Formation, or related data and analytics technologies.\nExperience developing REST APIs or lightweight web applications used by non-engineering audiences.\nExperience designing automated validation frameworks, data contracts, reusable data-processing libraries, or researcher-facing workflow tools.\nExperience with metadata-management, data-catalog, or data-discovery platforms, such as the AWS Glue Data Catalog, OpenMetadata, or Unity Catalog.\nExperience with containerization, continuous integration and deployment, or infrastructure-as-code.\nExperience supporting machine-learning workflows or preparing data for model development and evaluation.\nExperience working with large files or multimodal datasets, such as sequencing outputs, digital pathology images, clinical records, or experimental measurements.\nFamiliarity with governance considerations for research data, including access control, de-identification, and the handling of sensitive clinical information.\nExperience working within a data mesh, data product, or federated data-ownership model.\nWhat Success Looks Like:\nScientists spend less time manually locating, cleaning, interpreting, and restructuring data.\nResearch teams have well-supported, documented tools that help them prepare and share data consistently.\nData-quality problems are identified earlier through automated checks, validation at the point of handoff, and clearer documentation.\nApproaches you design become shared standards that multiple scientific teams adopt, rather than being rebuilt program by program.\nAWS capabilities are selected and applied thoughtfully in partnership with R&D DevOps, with clear ownership boundaries and repeatable deployment patterns.\nOther engineers work more effectively because of the patterns, reviews, mentoring, and documentation you contribute.\nScientific data becomes easier to discover, understand, analyze, and reuse across the organization.\nThe base pay for this position is\n$78,000.00 - $156,000.00In specific locations, the pay range may vary from the range posted.\nJOB FAMILY:\nProduct DevelopmentDIVISION:\nONCO Cancer DiagnosticsLOCATION:\nUnited States of America : RemoteADDITIONAL LOCATIONS:\nWORK SHIFT:\nStandardTRAVEL:\nYes, 10 % of the TimeMEDICAL SURVEILLANCE:\nNo SIGNIFICANT WORK ACTIVITIES:\nContinuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day), Keyboard use (greater or equal to 50% of the workday)Abbott is an Equal Opportunity Employer of Minorities/Women/Individuals with Disabilities/Protected Veterans.EEO is the Law link - English: http://webstorage.abbott.com/common/External/EEO_English.pdfEEO is the Law link - Espanol: 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