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 enabling 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 technical roadmap • Establish standards and reusable patterns for organizing and harmonizing data from disparate sources and drive adoption • 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 patterns, 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 on cross-project and cross-team designs, including design reviews and trade-off documentation • 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 appropriate distributed processing • 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 by scoping ambiguous problems, sequencing work, and making defensible decisions • Help scientists reduce manual data locating, cleaning, interpretation, and restructuring • Enable research teams to use documented, consistent data-preparation and sharing tools

🎯 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 several of the following: 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, such as statistical analysis, machine learning, computational modeling, or another data-intensive research activity • 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 • Demonstrated ability to investigate ambiguous problems, define an approach, and deliver a working solution with little guidance • Experience mentoring or providing technical guidance to other engineers, scientists, or analysts • Strong communication and collaboration skills across scientific and technical disciplines • 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 supporting research in life sciences, healthcare, diagnostics, or a similarly data-intensive and regulated scientific environment • Preferred: Production experience with Spark or PySpark and distributed data processing • Preferred: Depth in AWS services such as Athena, Glue, EMR, SageMaker, Lambda, Step Functions, Lake Formation, or related data and analytics technologies • Preferred: Experience developing REST APIs or lightweight web applications used by non-engineering audiences • Preferred: Experience designing automated validation frameworks, data contracts, reusable data-processing libraries, or researcher-facing workflow tools • Preferred: Experience with metadata-management, data-catalog, or data-discovery platforms • Preferred: Experience with containerization, continuous integration and deployment, or infrastructure-as-code • Preferred: Experience supporting machine-learning workflows or preparing data for model development and evaluation • Preferred: Experience working with large files or multimodal datasets • Preferred: Familiarity with governance considerations for research data • Preferred: Experience working within a data mesh, data product, or federated data-ownership model

🏖️ Benefícios

• Remote work arrangement • Standard work shift • Travel opportunity: 10% of the time