MMS

Are you looking to join a company where your contributions truly matter, and where you'll be part of a supportive, innovative team? MMS is a award-winning, data-focused clinical research organization (CRO). We pride ourselves on being a Great Place to Work certified organization, recognized for our exceptional culture and industry best employee retention rate. We support the pharmaceutical, biotech, and medical device industries with our proven, scientific approach to complex trial data and regulatory submission challenges. With a global footprint across four continents, MMS not only maintains an industry-leading customer satisfaction rating but also fosters a collaborative and inclusive work environment where employees can thrive. Join us at MMS and be part of a team that is shaping the future of clinical research.

Discover more about our exciting opportunities and why MMS is a great place to advance your career. Visit www.mmsholdings.com or follow MMS on LinkedIn.

Senior Clinical Data Engineer
The Senior Clinical Data Engineer leads the design and optimization of scalable clinical data engineering solutions that support regulatory compliance, advanced analytics, and operational excellence across global clinical trials. This role combines deep SQL and Python expertise with foundational knowledge of clinical trial processes, including protocol interpretation, CRF data structures, and CDISC standards such as SDTM and ADaM. The engineer partners across Biostatistics, Clinical Operations, and Regulatory Affairs to translate clinical requirements into secure, high‑performance data architectures and reusable engineering frameworks.

Key Responsibilities:

• Data Engineering & Architecture: Design and maintain secure, scalable relational databases and data lake architectures. Create reusable, highly parameterized Fabric Data Factory pipelines driven by project-based configuration files to orchestrate ingestion into Azure data lakes and staging into Microsoft SQL databases for downstream analytics.
•  Data Modeling & Warehousing: Apply data modeling best practices to transform source data into governed by common data models. Incorporate data warehouse concepts including star schemas to support dashboard reporting and implement data lineage strategies to enable auditability and inspection readiness.
• SQL & Performance Engineering: Develop advanced T-SQL stored procedures and leverage window functions, common table expressions, derived tables, and dynamic T-SQL. Optimize and tune complex queries and data processing workflows to ensure performance and scalability.
• Python-Centric Automation: Architect modular, maintainable Python-based codebases to support validation frameworks, edit checks, reconciliations, exception listings, protocol deviation detection, and resource projections. Think like a software developer by refactoring for reuse, applying design patterns, encapsulating logic, and minimizing side effects.
• Data Standardization & CDISC Governance: Lead enterprise-level mapping strategies that transform raw clinical data into standardized CDISC-compliant formats. Maintain strong knowledge of SDTM and ADaM structures and ensure consistent governance across studies.
• Data Quality & Validation: Design scalable validation frameworks that proactively detect systemic data issues. Prepare, correct, modify, and analyze large, complex datasets using advanced analytical techniques to ensure integrity and traceability.
• Regulatory Compliance & Documentation: Maintain a strong understanding of GCP, FDA 21 CFR Part 11, and evolving regulatory guidance as they pertain to data curation deliverables. Establish documentation standards, audit trail governance, and inspection-ready processes.
• Reporting & Analytics Enablement: Enable real-time dashboards and operational KPIs by delivering curated, high-quality datasets to analytics platforms. Support internal and external stakeholders through innovative tools and modern engineering solutions.

Minimum Requirements

• Bachelor’s degree in life sciences, statistics, data engineering, computer science, or related field, or equivalent experience.
• Minimum 7 years of experience in data engineering or a related discipline, preferably within clinical research or a CRO environment.
• ​​​​Advanced proficiency in SQL and Python; working knowledge of SAS and R • Strong understanding of clinical trial processes, protocols, CRF data, and CDISC standards (SDTM/ADaM).
• Experience designing Fabric and Azure-based data pipelines and Microsoft SQL database solutions.
• Demonstrated ability to mentor team members and collaborate cross-functionally.
• Strong analytical, organizational, and communication skills. 

• CRO experience as a Clinical Data Engineer or Programmer.
• Experience supporting integrated risk planning and management initiatives.