{"schemaVersion":"1.0","exportedAt":"2026-05-15T12:51:04.409Z","occupation":{"soc":"15-2051.02","title":"Clinical Data Managers","group":"Computer & Mathematical","sector":"54","jobZone":4,"jobZoneInferred":false},"framework":{"version":"v.26.05","description":"","contextCovered":"This framework covers clinical data management practice across the full lifecycle of regulated clinical trials — from initial database setup and data entry through query resolution, database lock, and regulatory submission — within pharmaceutical, biotech, CRO, and academic research environments.","levels":{"emerging":{"label":"Emerging","statements":["Clinical database entry tasks — execute under direct supervision following established data entry protocols in a regulated clinical trial environment.","Standard data receipt and filing procedures — apply consistently using approved electronic data capture systems on assigned study records.","Basic logic check results — recognize and flag discrepancies for senior review during routine data verification activities.","Pre-built data query templates — utilize to document and submit identified data omissions to the clinical data team.","Standard operating procedures for data management — follow precisely to ensure compliance during initial project assignments.","Existing data collection forms — interpret and populate accurately using study-specific instructions provided by senior staff.","Spreadsheet and office suite software — operate to prepare basic data activity listings under guidance from a supervising data manager.","Medical terminology and coding conventions — recognize and apply at a foundational level when processing clinical trial data.","Data formatting specifications — implement as directed when preparing assigned data sets for downstream analytical use.","Progress tracking reports — compile from provided templates to summarize routine data receipt and entry activities for team review."]},"developing":{"label":"Developing","statements":["Clinical database structures — design and configure with reduced oversight using validated database user interface and query software for mid-sized trial protocols.","Data validation logic checks — develop and test independently to identify entry errors across assigned study databases in a GCP-compliant environment.","Data queries — generate and manage in response to validation failures, resolving discrepancies by coordinating directly with clinical site staff.","Project-specific data management plans — draft covering coding conventions, data transfer schedules, and database lock procedures for single-protocol studies.","Routine data quality metrics — monitor and report to detect deviations from standard operating procedures across ongoing data management activities.","Custom data collection forms — design and refine to support efficient receipt, processing, and tracking of clinical data across assigned trials.","Analytical and categorization software — apply to prepare formatted data sets meeting sponsor or regulatory formatting requirements with minimal direction.","Database lock workflows — coordinate within a study team to ensure timely completion of data cleaning and readiness for statistical analysis.","Performance and progress reports — prepare independently by querying databases and summarizing data entry productivity metrics for project managers.","Time management and task prioritization — exercise routinely to balance simultaneous data management responsibilities across multiple active study protocols."]},"proficient":{"label":"Proficient","statements":["Complex clinical database architecture — design, validate, and optimize autonomously including advanced logic checks and edit specifications for large multi-site trials.","End-to-end data management plans — author and implement covering full lifecycle from data receipt through database lock, transfer, and regulatory submission.","Non-routine data discrepancies — resolve independently by applying deductive and inductive reasoning to assess root cause and coordinate corrective actions across clinical and statistical teams.","Cross-functional data workflows — evaluate and re-engineer using systems analysis techniques to improve efficiency and compliance in a clinical operations environment.","Programming scripts and queries — develop using object-oriented or scripting tools to automate validation, cleaning, and data transformation processes.","Regulatory and sponsor data standards — interpret and apply across all study deliverables, ensuring data sets conform to CDISC or equivalent frameworks.","Risk-based data monitoring plans — construct and execute to proactively identify data integrity issues before database lock across complex trial portfolios.","Advanced analytical listings and outputs — produce independently to support interim analyses, safety reviews, and final study reports for regulatory submissions.","Vendor and CRO data management activities — oversee and quality-assure to ensure external data pipelines meet contractual and protocol-defined standards.","Mentoring and technical guidance — provide to junior data managers on database design, query resolution, and SOP compliance within day-to-day project work."]},"advanced":{"label":"Advanced","statements":["Organizational data management strategy — define and lead across an enterprise clinical development portfolio, aligning practices with evolving regulatory and industry standards.","Clinical data management SOPs and governance frameworks — author and maintain at the organizational level to ensure consistent, audit-ready data quality practices.","Enterprise-wide database and technology infrastructure — evaluate, select, and champion including EDC platforms, analytical software, and data integration systems.","Departmental competency and talent development programs — design and implement to build clinical data management capability across emerging, developing, and proficient staff.","Executive and regulatory stakeholders — engage and advise on data integrity risks, database lock timelines, and data submission readiness for high-stakes regulatory filings.","Cross-departmental data governance committees — lead to establish enterprise data standards, change control processes, and system validation policies.","Complex problem escalations — resolve at the organizational level by applying systems evaluation and judgment to determine precedent-setting data management decisions.","Innovation and continuous improvement initiatives — drive by evaluating emerging technologies such as risk-based monitoring tools and AI-assisted data cleaning platforms.","Strategic partnerships with CROs, sponsors, and health authorities — negotiate and manage to align data management deliverables with organizational and regulatory objectives.","Organizational performance metrics and quality dashboards — design and champion to provide leadership visibility into data management productivity, compliance, and risk indicators."]}}},"sources":{"onet":"v30.2 (CC BY 4.0)","crosswalk":"https://skillscrosswalk.com","generator":"LER.me"},"attribution":"© EBSCOed"}