Senior Data Engineer - Hybrid

Mayo Clinic • Rochester, MN

18h • Hybrid

About The Position

The Senior Data Engineer joins Mayo Clinic’s Research Data Atlas team to build and advance its enterprise research data platform. Atlas centralizes, catalogs, integrates, harmonizes, and delivers complex research data to help investigators discover, access, combine, and analyze information across studies and data domains. The platform supports genomic and other omics data, biospecimens, clinical and phenotypic information, laboratory data, imaging, and other research data for translational research, precision medicine, artificial intelligence, and advanced analytics.

The role focuses on designing, building, and supporting scalable data products, pipelines, integrations, and services for complex biomedical research. The engineer works with large-scale genomic and multimodal datasets, translating investigator and scientific requirements into technically sound and scientifically appropriate solutions. Work includes ingesting and transforming research data; integrating clinical and research systems; harmonizing data structures and standards; maintaining metadata and provenance; validating data quality; and enabling secure, governed access to research datasets.

The position requires substantial research informatics experience, including genomic or other omics data, next-generation sequencing concepts, genomic formats, variants and annotations, normalization and harmonization, research-study and sample metadata, provenance, scientific data quality, and human-subjects research governance. The engineer works independently with bioinformaticians, computational scientists, investigators, laboratory personnel, and other research stakeholders.

The role uses Google Cloud Platform and cloud-native technologies, including BigQuery, Cloud Storage, Dataflow, Batch, and Pub/Sub. It also requires modern AI-assisted and agentic development tools such as Claude Code, OpenAI Codex, or comparable platforms for software development, data engineering, testing, troubleshooting, documentation, and code quality. The engineer develops and deploys pipelines, integrations, and transformations for analytics and machine learning applications as part of an assigned product team. They partner with product owners and Analytics and Machine Learning delivery teams to identify and retrieve data, conduct exploratory analysis, transform data, build and validate analytical models, and turn qualitative and quantitative assessments into actionable insights.

This is a hybrid position. The employee must be located within 100 miles of a Mayo Clinic campus for occasional on-site work based on business needs. The vacancy is not eligible for sponsorship, and Mayo Clinic does not participate in the F-1 STEM OPT extension program.

Requirements

• Bachelor’s degree in a relevant analytical or quantitative field and at least five years of professional or research experience in data visualization, data engineering, or analytical modeling techniques; alternatively, an associate’s degree in a relevant field and at least seven years of such experience. In-depth business or practice knowledge will also be considered.
• Advanced experience in SQL.
• Strong experience with scripting languages such as Python, JavaScript, PHP, C++, or Java, and API integration.
• Experience with hybrid batch and streaming data processing methods such as Apache Spark, Hive, Pig, or Kafka.
• Experience with big data, statistics, and machine learning.
• Ability to navigate Linux and Windows operating systems.
• Demonstrated experience designing, building, and installing data systems.
• Strong research informatics background, including substantial experience with genomic and/or other omics data, next-generation sequencing concepts, common genomic data formats, genomic variants and annotations, normalization and harmonization, study and sample metadata, provenance, scientific data quality, and human-subjects research data governance.
• Experience using Google Cloud Platform and technologies such as BigQuery, Cloud Storage, Dataflow, Batch, Pub/Sub, and related cloud-native services.
• Experience using modern AI-assisted and agentic development tools such as Claude Code, OpenAI Codex, or comparable platforms.
• Ability to manage a varied project workload with multiple priorities and stay current on healthcare trends and enterprise changes.
• Interpersonal, time management, and cross-functional teamwork skills.
• Strong analytical skills and ability to identify and recommend solutions, with a commitment to customer service.
• Excellent verbal and written communication, attention to detail, learning capacity, and problem-resolution skills.

Nice To Haves

• Knowledge of workflow scheduling, including Apache Airflow or Google Composer.
• Knowledge of infrastructure as code, including Kubernetes or Docker.
• Knowledge of CI/CD tools such as Jenkins or GitHub Actions.
• Experience in DataOps/DevOps and agile methodologies.
• Experience with hybrid data virtualization such as Denodo.
• Working knowledge of Tableau, Power BI, SAS, ThoughtSpot, DASH, d3, React, Snowflake, SSIS, and Google BigQuery.
• Google Cloud Platform certification.

Responsibilities

• Design, build, and support scalable data products, pipelines, integrations, and services for biomedical research use cases.
• Ingest and transform research data and integrate information across clinical and research systems.
• Harmonize disparate data structures and standards; maintain metadata and provenance; validate data quality; and enable secure, governed access to research datasets.
• Develop and deploy data pipelines, integrations, and transformations for analytics and machine learning applications and solutions.
• Work with large-scale genomic and multimodal datasets and translate investigator and scientific requirements into technically sound, scientifically appropriate solutions.
• Work independently with bioinformaticians, computational scientists, investigators, laboratory personnel, and other research stakeholders.
• Partner with product owners and Analytics and Machine Learning delivery teams to identify and retrieve data, conduct exploratory analysis, pipeline and transform data, and build and validate analytical models.
• Translate qualitative and quantitative assessments into actionable insights.
• Use AI-assisted and agentic development tools to support software development, data engineering, testing, troubleshooting, documentation, and code quality.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior