Senior Clinical Data Engineer — Remote Data Pipeline Lead

MiniMed

Watford

Hybride

GBP 70 000 - 110 000

Plein temps

Il y a 12 jours

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Résumé du poste

MiniMed seeks a Senior Clinical Data Engineer to join our Bioinformatics team, building scalable data pipelines for clinical research and real-world data initiatives. You will integrate data from multiple sources and transform raw data into analysis-ready datasets for analytics and decision-making.

Collaborate with Clinical Research, Biostatistics, R&D, and Software Engineering to ensure data quality and usability across a diverse data ecosystem.

Qualifications

  • Bachelor’s degree in Computer Science, Data Engineering, Biomedical Engineering, Statistics or a related field.
  • Minimum of 4 years of relevant experience in clinical data engineering, clinical data management, data analytics, or a related technical field.
  • Strong experience with SQL development and query optimization in Databricks and AWS Redshift.
  • Experience developing and maintaining data pipelines using Databricks, PySpark, AWS services, and AWS Redshift.
  • Experience working with structured and semi-structured data formats, including JSON and CSV.
  • Knowledge of clinical research data processes, clinical trial operations, and data quality principles.
  • Strong analytical, problem-solving, and troubleshooting skills.
  • Demonstrated ability to collaborate effectively across technical and non-technical teams.
  • Strong written and verbal communication skills.
  • Ability to work independently and manage multiple priorities in a fast-paced environment.

Responsabilités

  • 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.

Connaissances

SQL
Databricks
AWS
PySpark

Formation

Bachelor’s degree in Computer Science or related field

Outils

Git
CI/CD

Description du poste

MiniMed seeks a Senior Clinical Data Engineer to join our Bioinformatics team, building scalable data pipelines for clinical research and real-world data initiatives. You will integrate data from multiple sources and transform raw data into analysis-ready datasets for analytics and decision-making.

Collaborate with Clinical Research, Biostatistics, R&D, and Software Engineering to ensure data quality and usability across a diverse data ecosystem.

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