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Key Responsibilities and Required Skills for Clinical Data Analyst

💰 $75,000 - $120,000

Clinical DataHealthcareLife SciencesData Analytics

🎯 Role Definition

A Clinical Data Analyst is responsible for acquiring, cleaning, analyzing, and delivering high-quality clinical trial data to support clinical operations, biostatistics, regulatory submissions, and evidence generation. This role partners with clinical operations, data management, biostatistics, pharmacovigilance, and clinical programmers to ensure data integrity, compliance with CDISC and regulatory standards (FDA/EMA/ICH/GCP), and timely delivery of datasets, listings, and visualizations. The Clinical Data Analyst leverages EDC/CDMS tools (e.g., Medidata Rave, Oracle Clinical, REDCap), statistical software (SAS, R, Python), and data standards (SDTM, ADaM, CDASH) to transform raw clinical data into actionable insights.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Clinical Research Coordinator (CRC) transitioning into data-focused responsibilities.
  • Clinical Data Coordinator / Data Entry Specialist with EDC experience.
  • Junior Data Analyst or Biostatistics Assistant working on clinical studies.

Advancement To:

  • Senior Clinical Data Analyst
  • Clinical Data Manager / Lead Clinical Data Manager
  • Clinical Data Standards Lead / CDISC Specialist
  • Clinical Informatics Lead or Head of Clinical Data
  • Clinical Operations Manager or Project Manager (with cross-functional experience)

Lateral Moves:

  • Biostatistician / Clinical Programmer
  • Clinical Project Manager (PM)
  • Regulatory Affairs Specialist or Clinical Quality Assurance
  • Real-World Evidence (RWE) Data Analyst or Health Outcomes Analyst

Core Responsibilities

Primary Functions

  • Develop, maintain, and validate clinical trial datasets and derived variables in accordance with CDISC standards (SDTM and ADaM), ensuring datasets are analysis-ready for biostatistics and regulatory submission.
  • Perform end-to-end clinical data reconciliation across EDC, lab, ECG, PK, IVRS/IWRS and third-party vendor datasets; identify and resolve discrepancies and maintain audit trails for traceability.
  • Design, implement and validate extraction, transformation, and loading (ETL) processes and data pipelines to harmonize disparate clinical data sources and ensure data consistency.
  • Generate and maintain data listings, tables, figures and ad hoc datasets to support safety monitoring, interim analyses, DSMB reviews, and submission packages.
  • Build and execute comprehensive data validation plans, including edit checks, discrepancy management workflows, and data cleaning strategies to ensure high-quality, reliable trial data.
  • Configure, test, and document EDC/CDMS functionality (eCRF mappings, edit checks, skip logic) in collaboration with clinical data management and clinical operations teams to meet protocol specifications.
  • Translate clinical study protocols and case report forms (CRFs/eCRFs) into data model specifications, mapping requirements and data dictionaries for downstream use.
  • Perform statistical programming and data manipulation using SAS, R, or Python to support exploratory analysis, QA checks, and reproducible data transformations.
  • Conduct root cause analyses for data quality issues, propose mitigation plans, and implement preventive measures to reduce recurrence in future studies.
  • Prepare, review, and approve clinical data deliverables (e.g., SDTM/ADaM datasets, define.xml, dataset specifications, and send files) to support regulatory submission readiness.
  • Collaborate with biostatistics and clinical programming teams to ensure analysis dataset requirements are incorporated early and validated during study execution.
  • Manage vendor relationships and data transfers (labs, central labs, imaging, ePRO, wearables) including file specifications, validation checks, and secure transmission protocols.
  • Implement and maintain standard operating procedures (SOPs), work instructions, and study-specific data management plans to ensure compliance with GCP, ICH, and regulatory guidance.
  • Perform quality control and independent review of clinical data outputs, including peer review of programming code, dataset definitions, and reconciliation tables.
  • Create automated data quality reports, dashboards, and visualizations (Tableau, Power BI, ggplot2) to provide near real-time data insight for study teams and stakeholders.
  • Support safety surveillance by producing expedited safety datasets, MedDRA-coded adverse event summaries, and CTCAE-based severity listings for pharmacovigilance review.
  • Participate in cross-functional study team meetings to advise on data considerations for protocol amendments, enrollment strategies, and monitoring plans.
  • Implement data security and privacy best practices (HIPAA, GDPR) when handling patient-level data, including de-identification and controlled access procedures.
  • Maintain version control and reproducibility of data processing scripts and dataset builds using source control (Git) and documented change logs.
  • Lead or contribute to data standards and metadata initiatives, including creation and curation of controlled terminology, code lists, and standardized CRF libraries.
  • Support regulatory inspections and audits by preparing data-related artifacts, responding to data queries, and demonstrating compliance with documented processes.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Provide training and mentorship to junior data staff on CDISC standards, EDC systems, and data cleaning best practices.
  • Assist in feasibility assessments and study start-up by estimating data timelines, resource needs, and risk areas.
  • Support post-market and real-world evidence (RWE) projects by integrating registry and claims data with clinical study data.
  • Maintain up-to-date knowledge of regulatory guidance and industry best practices to continuously improve data processes.

Required Skills & Competencies

Hard Skills (Technical)

  • Proficient in SAS programming for clinical data manipulation, analysis dataset creation (ADaM), and QC (PROC SQL, DATA step, macros).
  • Strong SQL skills for querying and aggregating relational clinical databases and generating analysis-ready extracts.
  • Working experience with R or Python for reproducible data workflows, visualization (ggplot2, matplotlib), and data science tasks.
  • Hands-on experience with EDC/CDMS platforms such as Medidata Rave, Oracle Clinical/Argus, REDCap, or Veeva; ability to configure eCRFs and edit checks.
  • Deep knowledge of CDISC standards: CDASH, SDTM, ADaM and experience producing define.xml and metadata documentation.
  • Familiarity with medical coding dictionaries and terminologies: MedDRA, WHO Drug, and CTCAE; capability to support coding/quality checks.
  • Experience with data integration from external vendors (labs, central labs, imaging, ePRO, wearable devices) and validation of vendor-delivered datasets.
  • Proficiency in data cleaning, discrepancy management workflows, and maintaining audit trails and query resolution processes.
  • Experience preparing submission-ready datasets and documentation for health authority interactions (FDA/EMA) and clinical study reports.
  • Ability to build dashboards and visualizations in Tableau, Power BI, or similar tools to present clinical data metrics and KPIs.
  • Knowledge of regulatory requirements, GCP, ICH guidelines and data privacy laws (HIPAA, GDPR) as they relate to clinical data handling.
  • Familiarity with version control (Git), reproducible analytics workflows, and automation of data processes (CI/CD for clinical data pipelines).
  • Experience with statistical concepts, interim analysis support, and basic understanding of biostatistics to collaborate with study statisticians.

Soft Skills

  • Excellent written and verbal communication skills for interaction with clinical teams, vendors, and regulatory reviewers.
  • Attention to detail and a strong commitment to data quality and documentation.
  • Strong problem-solving skills and the ability to perform root cause analysis under time constraints.
  • Collaborative mindset with experience working in cross-functional teams (clinical operations, biostatistics, safety).
  • Proactive planner with solid organizational skills and ability to manage multiple studies and priorities concurrently.
  • Comfortable working in agile environments and adapting to changing study requirements.
  • Stakeholder management and ability to translate technical data concepts into business-focused insights.
  • Mentoring and training capabilities to uplift junior staff and improve team processes.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Life Sciences, Statistics, Biostatistics, Computer Science, Public Health, Nursing, or related quantitative discipline.

Preferred Education:

  • Master's degree in Biostatistics, Epidemiology, Data Science, Public Health, or Clinical Research.
  • Certifications such as Certified Clinical Data Manager (SCDM/CCDM), SAS Base/Advanced, or CDISC certifications are a plus.

Relevant Fields of Study:

  • Biostatistics
  • Epidemiology
  • Computer Science / Data Science
  • Nursing / Pharmacy / Life Sciences
  • Public Health

Experience Requirements

Typical Experience Range: 2–8 years of progressive clinical data or clinical research experience.

Preferred:

  • 3–5+ years of direct clinical data management/clinical data analysis experience supporting Phase I–IV studies or medical device trials.
  • Proven track record with CDISC standards (SDTM/ADaM) and submission deliverables.
  • Experience with global multi-center studies, vendor management, and regulatory submission support.
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