• Support hands-on preparation and analysis of scientific data when needed to understand a problem, validate a solution, or accelerate a research effort.

• Apply quantitative and scientific judgment when evaluating data, analytical requirements, and potential technical solutions.

• Use Spark or PySpark when distributed processing is appropriate for large or computationally intensive datasets.

• Apply and reinforce sound software-engineering practices, including version control, testing, code review, documentation, dependency management, continuous integration, and reproducible development.

• Communicate technical concepts, design decisions, trade-offs, limitations, and project status clearly to technical, scientific, and leadership audiences.

• Operate independently within an evolving environment: scope ambiguous problems, sequence the work, make defensible decisions when requirements are incomplete, and keep stakeholders informed.

Minimum Qualifications:

• 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, rather than one-time scripts or analyses.

• 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 with DevOps or cloud-engineering partners.

• 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 software-development practices such as 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, particularly when building alignment across scientific and technical disciplines.

Preferred Qualifications:

• Advanced degree in a quantitative, computational, or life-science discipline.

• Experience working with biomedical, genomic, clinical, proteomic, imaging, laboratory, or other complex scientific data.

• Experience supporting research in life sciences, healthcare, diagnostics, or a similarly data-intensive and regulated scientific environment.

• Production experience with Spark or PySpark and distributed data processing.

• Depth in AWS services such as Athena, Glue, EMR, SageMaker, Lambda, Step Functions, Lake Formation, or related data and analytics technologies.

• Experience developing REST APIs or lightweight web applications used by non-engineering audiences.

• Experience designing automated validation frameworks, data contracts, reusable data-processing libraries, or researcher-facing workflow tools.

• Experience with metadata-management, data-catalog, or data-discovery platforms, such as the AWS Glue Data Catalog, OpenMetadata, or Unity Catalog.

• Experience with containerization, continuous integration and deployment, or infrastructure-as-code.

• Experience supporting machine-learning workflows or preparing data for model development and evaluation.

• Experience working with large files or multimodal datasets, such as sequencing outputs, digital pathology images, clinical records, or experimental measurements.

• Familiarity with governance considerations for research data, including access control, de-identification, and the handling of sensitive clinical information.

• Experience working within a data mesh, data product, or federated data-ownership model.

What Success Looks Like:

• Scientists spend less time manually locating, cleaning, interpreting, and restructuring data.

• Research teams have well-supported, documented tools that help them prepare and share data consistently.

• Data-quality problems are identified earlier through automated checks, validation at the point of handoff, and clearer documentation.

• Approaches you design become shared standards that multiple scientific teams adopt, rather than being rebuilt program by program.

• AWS capabilities are selected and applied thoughtfully in partnership with R&D DevOps, with clear ownership boundaries and repeatable deployment patterns.

• Other engineers work more effectively because of the patterns, reviews, mentoring, and documentation you contribute.

• Scientific data becomes easier to discover, understand, analyze, and reuse across the organization.