Specialist, Data Architect

Merck; Województwo mazowieckie; On-site; PLN 180,000 - 240,000; Full time

Job summary

Merck is seeking a Specialist, Data Architect within GDMS Data Integration and Automation to design, integrate, and maintain clinical databases and systems. You will govern cross-study programming environments, manage libraries, code baselines, and ensure audit-ready documentation.

You will collaborate across teams to transform data into actionable insights, develop robust data models, and support validation, UAT, and release activities in a regulated environment.

Qualifications

• Foundational to intermediate-level proficiency in multiple programming languages (SQL, Python, SAS, R) for data workflows.
• Experience with data-management platforms and standard reporting/validation frameworks.
• Ability to transform clinical data into actionable insights and dashboards.
• Familiarity with SDLC, validation, and audit-ready documentation.

Responsibilities

• Design, build, test, and maintain clinical technology systems (EDC, RTSM, eCOA/ePRO) with guided automation.
• Develop advanced reporting solutions and dashboards with role-based access.
• Configure and validate data sources for integrations across systems.
• Mentor junior colleagues and support change control and release management.

Skills

SQL

Python

SAS

R

Tools

Data capture tools

Visualization tools

Reconciliation tools

Reporting tools

Validation tools

Job description

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.

Technical skills

• 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.

Non-technical skills

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


• Communicate eff