Senior Scientific Data Engineer, R&D Data Platform

🕒 Agosto 26

🌽 Illinois – Remoto

🌽 Illinois – Remote

💵 $78.000 - $156.000 / ano

⏰ Tempo Integral

🟠 Sênior

🚰 Engenheiro de Dados

🦅 Patrocina Visto H1B

Esta empresa já patrocinou vistos H1B no passado.

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Abbott

SiteLinkedInTodas as Vagas

10.000+ funcionários

Fundada em 1888

🏥 Saúde

⚕️ Seguro de Saúde

🧬 Biotecnologia

Healthcare • Healthcare Insurance • Biotechnology

A Abbott é uma empresa global de saúde comprometida em avançar tecnologias médicas e melhorar a vida das pessoas em todo o mundo. Oferece um amplo portfólio de produtos líderes em diagnósticos, dispositivos médicos, nutrição e medicamentos genéricos de marca. Inovações da Abbott, como os sistemas de monitoramento de glicose FreeStyle Libre e os testes rápidos de antígeno BinaxNOW, estão transformando o manejo do diabetes e a resposta à COVID-19. Por meio de parcerias e iniciativas, a Abbott busca promover a equidade em saúde, ampliar o acesso aos cuidados de saúde e enfrentar desafios críticos, como a desnutrição e as doenças infecciosas. A Abbott também é dedicada à sustentabilidade e à responsabilidade social, trabalhando para tornar tecnologias que mudam vidas acessíveis e economicamente viáveis.

Descrição

• 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

🎯 Requisitos

• 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

🏖️ Benefícios

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