Senior Clinical Data Engineer — ETL, Databricks & Python

MiniMed

Los Angeles (CA)

On-site

USD 110,000 - 150,000

Full time

2 days ago

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Job summary

MiniMed is seeking a highly skilled Data Engineer to design and maintain data pipelines for clinical and medical device data in a fast-paced environment. You will build ETL/ELT solutions using Databricks and PySpark, ingest JSON/CSV data, and transform it into analysis-ready datasets for statistical use.

Collaboration with Biostatistics and R&D teams is essential to align on data schemas and system integration.

Qualifications

SQL: Advanced proficiency - complex joins, window functions, CTEs, query optimization
Data Warehousing: Snowflake, Databricks, BigQuery, Redshift, Azure Synapse experience
Python: Proficient with pandas, NumPy, and visualization libraries
BI Tools: Power BI experience
Requires practical knowledge and demonstrated competence within job area typically obtained through advanced education combined with experience
Bachelor’s degree in computer science, Data Engineering, Biomedical Engineering, Statistics, or a related field with 4 years of relevant experience in clinical data engineering, clinical data management, data analytics, or a related technical field or 2 years with master’s degree

Responsibilities

Design, develop, evaluate, and maintain data pipelines, programs, and workflows used to process, validate, and analyze clinical and medical device data.
Build and support automated ETL/ELT solutions using Databricks, PySpark, SQL, and AWS services to ingest and transform structured and semi-structured data, including JSON and CSV files.
Collect, clean, transform, and standardize raw clinical and device data into high-quality, analysis-ready datasets for statistical analysis and reporting.
Partner with Biostatistics and study teams to understand analytical requirements and deliver finalized datasets optimized for statistical software and downstream use.
Collaborate with Medical Device and Software Engineering teams to align on data schemas, firmware updates, data payload specifications, and system integration requirements.
Identify data inconsistencies, perform root cause analysis, and implement solutions to ensure data quality, accuracy, and completeness.
Support clinical data management activities, including data mining, validation, reconciliation, and issue resolution across multiple data sources.
Contribute to the development and execution of data management plans that support study, project, and protocol timelines.
Provide technical support for software user acceptance testing and review training materials, instructions, and documentation related to clinical data systems.
Serve as a liaison among Clinical Research, R&D, Biostatistics, and other cross-functional stakeholders to communicate timelines, requirements, and deliverables.
Apply software engineering best practices, including version control, CI/CD, and code review processes, to ensure reliable and maintainable solutions.
Work independently on multiple projects in a deadline-driven environment while maintaining a high level of quality and accountability.
Develop and evaluate algorithms to characterize product/system performance, quality, data management, and accuracy.
Uses current programming language and technologies to translate algorithms and technical specifications into code.
Completes programming and implements efficiencies, performs testing and debugging.
Completes documentation and procedures for installation and maintenance.
Can work with large scale computing frameworks, data analysis systems, and modeling environments.