RWD Analytics Engineer

1 month ago


Mississauga, Ontario, Canada F. Hoffmann-La Roche Gruppe Full time

At F. Hoffmann-La Roche Gruppe, we're committed to fostering a culture of diversity, equity, and inclusion. This mindset is essential for understanding the complex healthcare needs of our global community. As a key member of our team, you'll play a vital role in harmonizing source data models to common data models like OMOP or FHIR. This may involve custom data models, depending on project requirements. You'll collaborate with clinical terminologists, medical informatics architects, data engineers, and data scientists to produce high-quality data sets for analysis or model building. Your expertise will also be crucial in creating and applying tests to the data pipeline, ensuring that mapped data retain their original semantics within specific use cases.

The Opportunity:

  • Syntactically and semantically map data to/from RWD common data models (e.g., OMOP, FHIR) using dbt or similar tools
  • Design, develop, and test ETL/ELT pipelines for real-world data
  • Provide complete documentation and communication of all processes, methods, and results
  • Integrate vocabularies/terminologies using REST APIs and/or via relational tables
  • Co-create scalable approaches for mapping data using declarative and functional programming approaches
  • Understand the complexities of RWD use cases from both business and IT/technical perspectives and develop a strategic approach to balance short-term benefits and long-term costs and contribute and share learnings throughout RWD communities
  • Partner with data scientists to understand and address their needs as well as with IT business domain leadership to influence product roadmaps

Requirements:

  • 5+ years of relevant experience and a Bachelor's or Master's degree in computer science, data science, informatics
  • Hands-on experience with healthcare data in at least one of the following disease areas: oncology, neuro, immunology, infectious diseases, ophthalmology, cardiovascular, and metabolism
  • Hands-on experience with real-world data, specifically OMOP
  • Experience with terminology integration, including OHDSI Athena and UMLS
  • Python (numpy, pandas, etc.) or R, SQL, CI/CD, git
  • (Preferred) Experience with semantic web technologies (e.g., SPARQL, RDF, OWL, Turtle)
  • Hands-on experience with relational databases and cloud platforms such as Snowflake
  • (Preferred) Experience with graph databases such as Neo4j and GraphDB and SDTM

Relocation benefits are not available for this posting.



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