Key Facts

Remote From:

India

Full time

Senior (5-10 years)

English

Hard Skills

Other Skills

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Team Leadership
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Communication
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Analytical Skills
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Organizational Skills
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Detail Oriented
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Problem Solving

Roles & Responsibilities

Bachelor's degree in life sciences, statistics, data engineering, computer science, or a related field, or equivalent experience.
Minimum 5 years of experience in data engineering or a related discipline, preferably within clinical research or a CRO environment.
Intermediate proficiency in SQL and Python; working knowledge of SAS and R; strong understanding of clinical trial processes, protocols, CRF data, and CDISC SDTM/ADaM.
Demonstrated ability to mentor team members and collaborate cross-functionally; strong analytical, organizational, and communication skills.

Requirements:

Data Modeling and Warehousing: apply data modeling best practices to transform source data into governed common data models; include star schemas and implement data lineage for auditability.
SQL/Performance Engineering: develop T-SQL procedures using window functions and CTEs; optimize complex queries and data workflows for performance and scalability; promote reusable code.
CDISC Data Standardization and Governance: lead enterprise-level mappings to transform raw clinical data into CDISC-compliant SDTM/ADaM formats and enforce governance across studies.
Data Quality and Regulatory Documentation: design scalable data validation frameworks; ensure data integrity and traceability; maintain documentation standards, audit trails, and inspection-ready processes.

Job description

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.

Clinical Data Engineer
The 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 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 intermediate 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.
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 5 years of experience in data engineering or a related discipline, preferably within clinical research or a CRO environment.
Intermediate 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).
Demonstrated ability to mentor team members and collaborate cross-functionally.
Strong analytical, organizational, and communication skills.

Preferred