This role pays less than
92%
of similar roles. Most pay
$138,343–$216,300
— the shaded band above.
At the midpoint, this role pays about
$117k
versus about
$177k
for comparable roles.
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
90 open roles
on FindRole.
Listed pay typically runs
$99,300–$198,700
across 89 roles with salary data.
Sr. Scientific Data Engineer, R&D Data Platform joins the Science Office within 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, scientific data, and applied analysis. You will own research data platform components end-to-end, building reusable tools, Python packages, data pipelines, APIs, notebooks, and lightweight web applications to help researchers organize, validate, and share complex genomic, clinical, imaging, and laboratory data. Key technologies include Python, SQL, Spark, PySpark, and AWS services such as S3, Athena, Glue, EMR, Lambda, and SageMaker. You will translate ambiguous scientific needs into durable capabilities, establish standards for harmonizing disparate data sources, and develop automated quality frameworks while mentoring other engineers in a research-focused environment to solve complex problems in the life sciences domain.
Design and deliver reusable tools, APIs, and data pipelines for ingesting, validating, and sharing complex scientific data.
Develop maintainable software solutions using Python and SQL to support research workflows and internal applications.
Translate ambiguous scientific requirements into prioritized technical roadmaps and durable platform capabilities.
Establish standards and reusable patterns for harmonizing heterogeneous data from disparate sources like genomic and clinical records.
Build automated data-quality and validation frameworks to identify inconsistencies before they reach downstream research.
Evaluate AWS services and partner with DevOps teams to implement scalable infrastructure for scientific data processing.
Lead technical design reviews, document trade-offs, and mentor other engineers through code reviews and peer feedback.
Improve the documentation, traceability, and discoverability of scientific datasets across the organization.
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 years of professional experience, or three years with an advanced degree in a relevant field.
Advanced programming skills in Python and strong SQL skills for structured and semi-structured data.
Proven track record of building reusable, maintainable software including data pipelines, Python packages, APIs, and internal tools.
Substantial hands-on experience using AWS services for data processing, analytics, and scientific computing.
Experience conducting or supporting quantitative research such as statistical analysis, machine learning, or computational modeling.
Fluency in software development practices including Git, automated testing, technical documentation, and continuous integration.
Advanced degree in a quantitative, computational, or life-science discipline (preferred).
Python
SQL
Scala
Java
Azure
AWS
GCP
Microsoft Fabric
Azure Databricks
Snowflake
Big Query
Azure Data Factory
Air Flow
Cosmos DB
Azure Functions
GitHub
Git
CI/CD
Azure DevOps
Data Modeling
Python
Scala
Java
SQL
Azure
AWS
GCP
Azure Synapse
Azure Databricks
Redshift
Big Query
Snowflake
Azure Data Factory
Cosmos DB
Event Hubs
Azure Storage
Azure Functions
Container Apps
CI/CD
Azure DevOps
GitHub
Git
ETL
ELT
Data Modeling
Data Warehousing
Data Lake
Test Driven Development