Clinical Data Engineering Specialist

Merck Merck · Pharma · Cundinamarca, Colombia

This role focuses on building, integrating, and maintaining clinical databases and study systems within the healthcare domain. It involves designing, developing, and validating clinical technology systems, implementing complex data logic, and creating advanced reporting solutions. The position requires strong programming skills in SQL, Python, SAS, and R, along with a solid understanding of clinical data flow, data standards, and validation practices. The role also involves collaborating with stakeholders, troubleshooting data issues, and ensuring adherence to SOPs and regulations.

What you'd actually do

  1. Build, integrate, and maintain clinical databases and study systems, including transformations, edit checks, reporting, and user testing, while supporting governance of cross‑study programming environments.
  2. Design, develop, test, and maintain clinical technology systems (EDC, RTSM, eCOA/ePRO) using programming and automation frameworks.
  3. Implement and validate complex data logic such as rules, derivations, and dynamic rules.
  4. Propose and develop tools to streamline design, build, and validation workflows.
  5. Apply data literacy to convert collected data into actionable insights.

Skills

Required

  • SQL
  • Python
  • SAS
  • R
  • Clinical Data Management
  • Database Development
  • Data Processing
  • Data Quality Assurance
  • Data Validation
  • Data Visualization
  • Electronic Data Capture (EDC)
  • PL/SQL

Nice to have

  • Adaptability
  • Clinical Data Cleaning
  • Customer-Focused
  • Data Analysis
  • Data Review
  • Learning Agility
  • Pharmacovigilance

What the JD emphasized

  • Minimum of 3 years experience working in database configuring, data engineering or data management operations, or 1+ years of specific experience in clinical database programming and upstream/downstream clinical data model configuring
  • Proficiency in multiple programming languages (SQL, Python, SAS, R) for implementing data collectors, integrations, transformations, and reporting.
  • Strong understanding of clinical data flow across the study lifecycle (collection, processing, review, reconciliation, reporting).
  • Familiarity with clinical and regulatory data standards, clinical operations, and typical clinical trial data structures.
  • Knowledge of SDLC and validation practices, including change control, release checklists, and traceability.
  • Understanding of GCP, data integrity, audit‑ready documentation, and UAT/sign‑off procedures.