Data Engineering Lead for Pharma ML Pipelines (Hybrid)

Merck; Rahway (NJ); On-site; USD 129,000 - 203,000; Full time

Benefits offered by this job

Annual bonus

Medical, dental, vision coverage

401(k) retirement plan

Paid holidays and vacation

Job summary

Merck’s DSCS Digital Technologies team seeks a data engineer to design, build, and maintain data pipelines capturing SPD experimental data for ML and modeling workflows. The role is onsite in Rahway, NJ with hybrid flexibility across scientific, engineering, and digital disciplines.

The candidate will automate ingestion from ELN/LIMS and instruments, define data standards, and deliver analytics-ready datasets to modeling teams, enabling faster insights while upholding data governance and

Qualifications

• Ph.D./M.S./B.S. with industrial/pharmaceutical experience or equivalent.
• Experience building data pipelines and ETL/ELT in a scientific context.
• Proficiency in Python and/or R with Jupyter/Posit/RStudio.

Responsibilities

• Design, build, and maintain data pipelines for SPD experimental data.
• Automate ingestion from laboratory instruments and ELN/LIMS systems.
• Define data standards and ensure interoperability for modeling workflows.
• Collaborate with SPD scientists and engineers to deliver data-ready datasets.

Skills

Python

R

communication

team collaboration

data modeling

Education

Ph.D. in CS/DS/Engineering/Pharmaceutical Sciences

M.S. in CS/DS/Engineering/Pharmaceutical Sciences

B.S. in CS/DS/Engineering/Pharmaceutical Sciences

Tools

Dataiku

Databricks

Graph databases

AWS (S3, Redshift, Glue, Athena, SageMaker)

ELN, LIMS, PAT

Job description

Merck’s DSCS Digital Technologies team seeks a data engineer to design, build, and maintain data pipelines capturing SPD experimental data for ML and modeling workflows. The role is onsite in Rahway, NJ with hybrid flexibility across scientific, engineering, and digital disciplines.

The candidate will automate ingestion from ELN/LIMS and instruments, define data standards, and deliver analytics-ready datasets to modeling teams, enabling faster insights while upholding data governance and