Specialist, Data Architect

Job Description

The Specialist, Data Architect, GDMS Data Integration and Automation executes building, integrating, and maintaining clinical databases / clinical operational systems.  In addition, this role executes clinical study systems setup, transformations, integrations, edit checks, reports, and user testing - aligned to standards and study documentation. The Specialist, Data Architect is involved in governance of cross-study programming environments, including management of libraries, code baselines, and terminologies and management of in-life support, change/incident tracking, while driving continuous improvement.

Responsibilities

Programming and reporting

·       Design, build, test, and maintain clinical technology systems (e.g., EDC [Electronic Data Capture], RTSM [Randomization & Trial Supply Management], eCOA/ePRO [electronic Clinical Outcome Assessment/ electronic Patient-Reported Outcome]) with moderate guidance using programming / reporting frameworks and automation tools, ensuring quality and compliance.

·       Implement and validate complex data logic (edit checks, derivations, dynamic rules) using scripting and validation frameworks, with guidance as needed.

·       Propose and implement tools to streamline design, build, and validation processes.

·       Apply data literacy to transform collected data into actionable insights.

·       Develop advanced reporting solutions (dashboards, listings, visual analytics) using enterprise platforms; incorporate reusable components and role-based access.

Data Engineering

·       Configure, test, and validate data sources for integrations and transformations across internal and external systems, using standard procedures and with guidance as needed.

·       Collaborate with stakeholders to translate business needs into data and system requirements.

·       Troubleshoot straightforward issues in data ingestion and consumption processes and apply standard fixes, escalating non-routine problems to senior colleagues.

·       Develop robust data models, semantic layers, and curated datasets; monitor pipelines and enforce quality checks using established procedures with moderate guidance.

Process

·       Validate system functionality and ensure compliance with SOPs (Standard Operating Procedures) and regulations.

·       Identify process gaps and contribute to continuous improvement initiatives.

·       Maintain documentation for changes and incidents to support audit readiness.

·       Mentor junior colleagues, share best practices, and support onboarding and training.

·       Support release management processes for various business needs across environments (data management/clinical operations), including change control and risk assessment.

·       Align stakeholders by translating requirements into technical specs; lead UAT (User Acceptance Testing), capture feedback, and improve reporting standards.

·       Partner across teams to turn data and technical findings into clear, actionable stories

·       Act as a reliable partner to GDMS (Global Data Management & Standards) and other cross-functional teams, actively seeking input, aligning on priorities, and closing feedback loops to drive high-quality, coordinated delivery.

·       Foundational to intermediate-level proficiency in more than one programming language (e.g., SQL, Python, SAS, and R) for implementing clinical data collectors, integrations, transformations, reporting and visualizations

·       Proficiency with key tools and platforms used in the data-management ecosystem (e.g., data capture, review/visualization, reconciliation, reporting, or validation tools), with the ability to learn new technologies with guidance.

·       Ability to execute technical requirements on straightforward tasks independently, seeking moderate guidance for non-standard work.

·       Experienced in scripting and automating data ingestion, cleaning, and reporting processes using established methods, with guidance for complex automation.

·       Strong working knowledge of how clinical data are collected, processed, reviewed, reconciled, and reported across the study lifecycle.

·       Familiarity with common clinical and regulatory data standards, clinical operations, and data structures typical of clinical trials and related sources (e.g., electronic forms, external data feeds, operational systems).

·       Knowledge of System Development Lifecycle and validation practices (change control, release checklists, traceability from requirements to test evidence).

·       Understanding of Good Clinical Practice (GCP), data integrity, and audit-ready documentation; familiarity with UAT and sign-off processes.

·       Experience in report performance optimization (query tuning, filters/parameters) and data quality controls for reporting and analytics.

·       Ability to connect business outcomes to technical deliverables on straightforward problems, using standard procedures and seeking guidance for complex cases.

·       Prioritize tasks, meet deadlines, maintain clean documentation of assumptions, decisions, and next steps.

·       Communicate effectively (oral/written) with technical and business partners and external service providers; adapt style to audience.

·       Work independently with minimal guidance; triage issues using data; proactively flag risks and propose options.

·       Collaborate smoothly across functions; facilitate small working sessions; follow through on action items.

·       Establish positive working relationships across functions; receptive to feedback.

·       Execution Excellence | Focus on self-development, openness to feedback, and quickly building skills; embrace continuous learning and actively identify new areas for growth

·       Decision Making | Identify and analyze problems by interpreting qualitative and quantitative information, select best course of action, and make timely decisions while considering tradeoffs; optimize probable consequences

·       Networking & Partnerships | Build knowledge of the company, processes, clients, and customers; work across functions and collaborate effectively; leverage relationships within and across work groups to achieve results

·       Non-Degree with a minimum of 4 years of experience working in clinical data programming, data engineering, or related data-management/clinical operations functions.

·       [Minimum preferred] Associate's Degree with a minimum of 3 years’ experience working in clinical data programming, data engineering, or related data-management/clinical operations functions.

·       Bachelor's Degree with a minimum of 2 years’ experience working in clinical data programming, data engineering, or related data-management/clinical operations functions.

·       Advanced Degree (Masters or Doctorate) with prior experience in clinical development and / or clinical data programming, data engineering, or related data management/clinical operations activities preferred.

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