below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$126,800–$214,000.
Based on 240 similar postings.
Employer
About Abbott
Abbott Laboratories is a global healthcare company that manufactures and markets a broad and diversified line of health care products including diagnostics, medical devices, nutritionals, and branded generic pharmaceuticals. Industry: Healthcare & Medical Devices
Abbott currently has
193 open roles
on FindRole.
Listed pay typically runs
$99,300–$198,700
across 123 roles with salary data.
The Sr. Scientific Data Engineer, R&D Data Platform joins the Science Office within Abbott Cancer Diagnostics to lead the design and delivery of practical data solutions for cancer research and diagnostic development. This role sits at the intersection of software engineering and scientific data, requiring the owner to build reusable tools, data pipelines, APIs, and lightweight web applications. You will develop maintainable solutions using Python, SQL, and Spark or PySpark to organize, validate, and transform heterogeneous research data, including genomic, clinical, imaging, and laboratory datasets. Key responsibilities include establishing standards for harmonizing disparate data sources, designing automated quality frameworks, and evaluating AWS services like S3, Athena, Glue, EMR, Lambda, and SageMaker. You will translate ambiguous scientific needs into durable technical capabilities while mentoring engineers and collaborating with bioinformaticians and DevOps partners.
Design and deliver reusable tools and services for ingesting, validating, transforming, and sharing scientific data.
Own platform capability areas end-to-end, including design, implementation, operational support, and long-term maintainability.
Develop maintainable solutions using Python and SQL, such as data pipelines, APIs, and lightweight internal applications.
Create self-service workflows that allow researchers with varying programming experience to prepare and share data consistently.
Partner with scientific teams to translate research needs into a prioritized technical roadmap.
Establish standards and reusable patterns for organizing and harmonizing data from disparate sources.
Design automated data-quality and validation frameworks to identify malformed or inconsistent data.
Evaluate AWS services and partner with DevOps teams to implement architecture and deployment patterns.
What we're looking for
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 and strong SQL skills for working with structured and semi-structured data.
Demonstrated track record of building reusable, maintainable software and experience delivering data pipelines, Python packages, APIs, or analytical workflows.
Substantial hands-on experience using AWS for data processing, analytics, scientific computing, or software development.
Experience conducting or supporting quantitative research, such as statistical analysis, machine learning, or computational modeling.
Fluency with software-development practices including Git, automated testing, technical documentation, code review, and continuous integration.
Experience mentoring or providing technical guidance to other engineers, scientists, or analysts.
AWS
Python
SQL
Spark
Pyspark
Databricks
EMR
Data Pipelines
Data Warehouse
Data Lake
APIs
GitHub
Machine Learning
Business Intelligence
Cloud Architecture