Senior Scientific Data Engineer, R&D Data Platform

🔥 0 minutes ago

🌽 Illinois – Remote

🌽 Illinois – Remote

💵 $78k - $156k / year

⏰ Full Time

🟠 Senior

🚰 Data Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

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Abbott

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1888

🏥 Healthcare

⚕️ Healthcare Insurance

🧬 Biotechnology

Healthcare • Healthcare Insurance • Biotechnology

Abbott is a global healthcare company committed to advancing medical technologies and improving lives around the world. It offers a broad range of leading products in diagnostics, medical devices, nutrition, and branded generic medicines. Abbott's innovations such as the FreeStyle Libre glucose monitoring systems and BinaxNOW rapid antigen tests are transforming diabetes management and COVID-19 response. Through its partnerships and initiatives, Abbott aims to foster health equity, improve access to healthcare, and address critical health challenges like malnutrition and infectious diseases. Abbott is also dedicated to sustainability and social responsibility, striving to make life-changing technologies accessible and affordable.

📋 Description

• Lead the design and delivery of reusable tools and services for ingesting, validating, transforming, documenting, discovering, and sharing scientific data • Own platform capability areas end to end, including design, implementation, adoption, operational support, and long-term maintainability • Develop Python and SQL solutions including software packages, data pipelines, APIs, notebooks, workflow utilities, and lightweight internal applications • Create self-service workflows for researchers to prepare and share data consistently • Partner with scientific teams to understand studies, analytical workflows, data sources, and technical challenges, translating needs into a prioritized roadmap • Establish standards and reusable patterns for organizing and harmonizing disparate data sources • Design automated data-quality and validation frameworks • Improve documentation, traceability, and discoverability of scientific datasets • Evaluate AWS services and features and partner with R&D DevOps on architecture, deployment, and operational ownership • Develop solutions using Amazon S3, Athena, Glue, EMR, Lambda, and SageMaker • Prototype research-program solutions and generalize successful approaches into reusable platform capabilities • Provide technical leadership through design reviews, trade-off documentation, and cross-team alignment • Mentor engineers through code review, pairing, design feedback, and documentation • Support hands-on preparation and analysis of scientific data • Apply quantitative and scientific judgment to data and technical solutions • Use Spark or PySpark for suitable large or computationally intensive datasets • Apply software-engineering practices including version control, testing, code review, documentation, dependency management, continuous integration, and reproducible development • Communicate technical concepts, decisions, trade-offs, limitations, and project status to technical, scientific, and leadership audiences • Operate independently in an evolving environment and keep stakeholders informed

🎯 Requirements

• Bachelor’s degree in computer science, data science, engineering, statistics, mathematics, bioinformatics, computational science, or another relevant quantitative discipline • Five or more years of relevant professional or applied research experience, or three or more years with an advanced degree in a relevant field • Advanced programming skills in Python • Strong SQL skills and experience working with structured and semi-structured data • Demonstrated track record of building reusable, maintainable software that others depend on • Experience designing and delivering data pipelines, Python packages, APIs, analytical workflows, notebooks, or internal software tools • Substantial hands-on experience using AWS for data processing, analytics, scientific computing, or software development • Sufficient depth in AWS services and architecture to evaluate technical options, justify design recommendations, and define infrastructure requirements • Experience conducting or supporting quantitative research • Experience cleaning, integrating, standardizing, or validating data from multiple sources at meaningful scale • Fluency with Git, automated testing, technical documentation, code review, and continuous integration • Ability to investigate ambiguous problems, define an approach, and deliver a working solution with little guidance • Experience mentoring or providing technical guidance to engineers, scientists, or analysts • Strong communication and collaboration skills • Preferred: advanced degree in a quantitative, computational, or life-science discipline • Preferred: experience with biomedical, genomic, clinical, proteomic, imaging, laboratory, or other complex scientific data • Preferred: experience in life sciences, healthcare, diagnostics, or a similarly data-intensive and regulated scientific environment • Preferred: production experience with Spark or PySpark • Preferred: depth in AWS services such as Athena, Glue, EMR, SageMaker, Lambda, Step Functions, Lake Formation, or related technologies • Preferred: REST APIs or lightweight web applications • Preferred: automated validation frameworks, data contracts, reusable data-processing libraries, or researcher-facing workflow tools • Preferred: metadata-management, data-catalog, or data-discovery platforms • Preferred: containerization, CI/CD, or infrastructure-as-code • Preferred: machine-learning workflows or model-development data preparation • Preferred: large files or multimodal datasets • Preferred: research-data governance, access control, de-identification, and sensitive clinical information • Preferred: data mesh, data product, or federated data-ownership model

🏖️ Benefits

• Remote work arrangement • Travel: 10% of the time