Data Engineer
Mass General Brigham
Mass General Brigham relies on a wide range of professionals, including doctors, nurses, business people, tech experts, researchers, and systems analysts to advance our mission. As a not-for-profit, we support patient care, research, teaching, and community service, striving to provide exceptional care. We believe that high-performing teams drive groundbreaking medical discoveries and invite all applicants to join us and experience what it means to be part of Mass General Brigham.
While experience with ML Ops/DevOps practices is beneficial, direct machine learning experience is not required. We prioritize enthusiasm, ownership, and eagerness to learn.
Principal Duties and Responsibilities:
- Maintain and continuously develop our ML platform, ensuring reliable operation and scalability.
- Integrate ML models seamlessly into clinical workflows, with a particular focus on integration within Epic EHR systems.
- Implement and manage ML Ops best practices, including model deployment, monitoring, version control, and reproducibility.
- Facilitate the deployment, scaling, and management of dockerized pipeline containers
- Lead the development and improvement of user interfaces (ReactJS)
- Expand the ML platform’s capabilities, including support for Large Language Models (LLMs).
- Perform SQL-based data queries for efficient ingestion, extraction, and preparation of datasets.
- Generate and maintain high-quality datasets tailored to ML research and clinical studies.
- Oversee quality assurance processes, documentation standards, and best practices for the ML platform infrastructure.
Job Summary
SummaryResponsible for implementing methods to improve data reliability and quality. They combine raw information from different sources to create consistent and machine-readable formats and they develop and test architectures that enable data extraction and transformation for predictive or prescriptive modeling.
Does this position require Patient Care?
No
Essential Functions
-Design, develop, and implement data pipelines and ETL/ELT code to support business requirements.
-Maintain and optimize various components of the data pipeline architecture.
-Deliver high quality, efficient solutions to meet technical standards and industry best practices.
-Deliver optimal technical solutions for business and operational requirements.
-Participate in team design sessions and contribute options and solutions Produce and support product documentation.
-Participate in ETL Quality circle discussions to explore, discuss, and arrive at efficient solutions and best practices.
Qualifications
Education
Bachelor's Degree Computer Science required or Bachelor's Degree Related Field of Study required
Can this role accept experience in lieu of a degree?
Yes
Licenses and Credentials
Experience
Data warehousing development in large reporting environment(s) 2-3 years required and Experience with developing data pipelines using on Snowflake features ( Snowpipe, SnowSQL, Snow Sight, Data Streams ) required and Hands-on development experience with ETL/ELT tools, such as dbt, Fivetran, or Informatica required and Experience working in Agile software development environment required
Knowledge, Skills and Abilities
- Working knowledge of cloud computing platforms such as AWS, GCP, or Azure.
- Familiarity with enterprise data warehousing systems a plus.
Additional Job Details (if applicable)
Remote Type
Work Location
Scheduled Weekly Hours
Employee Type
Work Shift
Pay Range
$73,798.40 - $107,400.80/Annual
Grade
6
EEO Statement:
Mass General Brigham Competency Framework
At Mass General Brigham, our competency framework defines what effective leadership “looks like” by specifying which behaviors are most critical for successful performance at each job level. The framework is comprised of ten competencies (half People-Focused, half Performance-Focused) and are defined by observable and measurable skills and behaviors that contribute to workplace effectiveness and career success. These competencies are used to evaluate performance, make hiring decisions, identify development needs, mobilize employees across our system, and establish a strong talent pipeline.