Clinical Data Engineer (Lifesciences) (Hybrid) (Bangalore)

Job Title - Clinical Data Engineer (Lifescience domain )

Job Type - Hybrid

Job Location - Bangalore

Experience - 7+ Years

Mandatory skills- Life science domain, SDTM /CDISC/Adam, Databricks, CI/CD pipeline, R studio, Git hub

Roles & Responsibilities

Person has to collaborate with scientific team

Balance data structure

Support data team, process data pipeline efficiently.

Detailed Job Description -

Role summary

We are seeking a highly skilled clinical Data Engineer . You will combine clinical data expertise with strong data engineering and technical skills to generate well documented pipelines from source to curated data sets in common data models like CDISC SDTM. You will collaborate closely with clinical SMEs, data scientists, infrastructure, and other skilled data engineers.

We are looking to expand this functionality to include Real World Data (from a broad range of regristries). You will help extend our medallion Databricks pipelines (CDISC SDTM) to incorporate Real‑World Data (RWD) from registries and other sources, working with clinical experts and AI teams to combine rule‑based and automated mapping approaches (including OMOP interoperability).

Key responsibilities

* Design, build and maintain production ETL pipelines in Databricks/Delta Lake to ingest RWD (registries, claims, EHR extracts) and transform into standard models.

* Implement harmonisation workflows to map incoming RWD to OMOP and to the internal CDISC SDTM canonical model; handle vocabulary mapping, units normalization and provenance.

* Extend the medallion architecture (bronze/silver/gold) patterns with robust validation, lineage, partitioning and performance tuning.

* Develop configurable, inputdriven transformation frameworks so clinical experts can drive mapping rules via config files and catalogs.

* Integrate AI/automation components (e.g., model-assisted mapping, NLP for free text) with humanintheloop review and confidence scoring.

* Establish testing, CI/CD, monitoring and alerting for ETL jobs and automations; ensure reproducibility, versioning and governance.

* Collaborate with clinical data scientists, data stewards and stakeholders to define requirements, data contracts and success metrics.