• Build and maintain data ingestion pipelines using Azure Data Factory (ADF) to move data into the corporate Azure Data Lake and related platforms. • Develop and enhance data transformation logic in Databricks notebooks, primarily leveraging SQL-based transformations in existing SQL scripts and stored procedures. • Support a medallion data architecture (Bronze, Silver, Gold) with Silver and Gold datasets stored primarily in Parquet format. • Assist in migrating SQL Server–based processes (scripts, stored procedures, SSIS logic) running on Azure VMs into Azure-native pipelines and Databricks transformations. • Continuously update and enhance existing database solutions and systems based on new and evolving requirements from marketing campaign activation and analytics teams, ensuring data remains accurate, timely, and fit for purpose. • Manage multiple concurrent initiatives, including modernization efforts and ongoing enhancement requests, while maintaining platform stability and data quality. • Configure Azure Data Factory pipelines to orchestrate Databricks notebooks and Fabric-related processes as applicable. • Use Azure DevOps for source control, versioning, and deployment of Azure Data Factory, Databricks, and Fabric assets. • Test, monitor, and troubleshoot pipelines to ensure data reliability, performance, and quality. • Collaborate with senior engineers, project managers, campaign services, analytics, and platform teams to deliver ongoing data solutions. Participate in the adoption and use of Microsoft Fabric, understanding how Fabric components (e.g., OneLake, Lakehouse, Warehousing) integrate with existing Azure and Databricks workflows. • Maintain technical documentation for data pipelines, transformations, and datasets

Title: Manager, Platform Data Architecture Location: Remote North Carolina United States of America Full time Remote At Thermo Fisher’s PPD clinical research business (CRG), we’re using digital innovation, data science, and AI to reimagine how life-changing therapies reach patients. Our teams combine deep scientific expertise with advanced analytics, automation, and digital platforms to make research smarter, faster, and more connected. We know that innovation happens when diverse minds meet. Our Digital Science, Data, and AI professionals collaborate closely with scientists, clinicians, and operational experts to solve real-world challenges in clinical research. Alongside our partnership with Open AI, you can be part of the collaboration that will help to improve the speed and success of drug development, enabling customers to get medicines to patients faster and more cost effectively. You’ll join a culture that values experimentation, learning, and collaboration — where your ideas can help shape how we deliver life-saving solutions and improve global health outcomes. Manager, Platform Data Architecture The Manager, Platform Data Architecture serves as a central point of accountability for data architecture, data modeling and cross-platform alignment for Clinical Analytics Data Modeling, including ensuring data integrity, traceability, and audit readiness. This is a hands-on role that leads a small team of developers, ensuring alignment with business objectives and enterprise data strategy. The ideal candidate brings deep technical expertise in modern data ecosystems along with strong leadership and cross-functional collaboration skills. This is a home-based position for candidates on the East Coast US, EST time zone. Key Responsibilities - Manage and mentor a team of data developers, fostering technical excellence and professional growth - Define and enforce data architecture standards, best practices, and governance frameworks - Partner with business stakeholders, IT leadership, and product teams to translate business requirements into scalable data solutions - Oversee data integration strategies across internal and external systems (ETL/ELT, APIs, streaming) - Ensure data quality, security, privacy, and compliance with regulatory standards (e.g., GDPR, HIPAA as applicable) - Support analytics, AI/ML, and reporting initiatives by enabling accessible and well-structured data assets - Develop and maintain data engineering architecture documentation, including design specifications, data flow diagrams, and interface guidelines. - Monitor and troubleshoot data engineering processes, identifying and resolving issues to ensure reliable and efficient data flow. - Drive continuous improvement in data engineering practices, adopting new technologies and methodologies to enhance the engineering process. - Provide regular updates and reports to senior management on data engineering projects, status, risks, and opportunities. - Foster a collaborative and innovative team culture, encouraging professional growth and development. Education and Experience: - Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (Advanced degree preferred) - Previous data architecture or data engineering experience that provides the knowledge, skills, and abilities to perform the job (comparable to 8+ years’ experience). - 2+ years of experience managing technical teams - In some cases an equivalency, consisting of appropriate education, training, and/or directly related experience will be considered sufficient for an individual to meet the requirements of the role. Knowledge, Skills and Abilities: - 5+ years of hands-on experience with DBT/Python, Talend and Snowflake/Databricks in production environment - Solid SQL and understanding of database technologies like Snowflake, Databricks - Excellent problem-solving skills and the ability to think strategically. - Strong experience with cloud platforms (AWS, Azure, or GCP) and modern data stack technologies - Expertise in data modeling, database design (SQL/NoSQL), and data integration frameworks - Experience with big data technologies (e.g., Spark, Hadoop, Kafka) - Solid understanding of data governance, data quality, and security best practices - Experience in regulated industries (e.g., healthcare, life sciences, finance) (preferred) - Familiarity with data mesh or data fabric architectures (preferred) - Certifications in cloud platforms or data architecture (preferred) - Demonstrated leadership abilities with a focus on team development - Proven track record in process improvement and project management - Strong analytical and problem-solving skills - Excellent communication abilities across all organizational levels - Ability to manage multiple priority projects simultaneously At Thermo Fisher Scientific, we are committed to fostering a healthy and harmonious workplace for our employees. We understand the importance of creating an environment that allows individuals to excel. Please see below for the required qualifications for this position, which also includes the possibility of equivalent experience: - Able to communicate, receive, and understand information and ideas with diverse groups of people in a comprehensible and reasonable manner. - Able to work upright and stationary for typical working hours. - Ability to use and learn standard office equipment and technology with proficiency. - Able to perform successfully under pressure while prioritizing and handling multiple projects or activities. - May require as-needed travel (0-20%). Location: Remote East Coast USA (EST time zone). Relocation assistance is NOT provided. *Must be legally authorized to work in the United States without sponsorship. *Must be able to pass a comprehensive background check, which includes a drug screening. The annual salary range estimated for this position in North Carolina is $110,000 - $150,000 USD. This position may also be eligible to receive a variable annual bonus based on company, team, and/or individual performance results in accordance with company policy.

• Build and maintain data models and transformation workflows that support analytics, reporting, and product use cases. • Support the development and improvement of data pipelines and systems to process and analyze healthcare data, gaining experience across the full data engineering lifecycle from ingestion and transformation to storage and consumption. • Work with Python and SQL to transform, validate, and troubleshoot data across our platform. • Contribute to our Databricks-based analytics platform and help improve the usability, quality, and documentation of our datasets. • Collaborate with cross-functional teams to understand their data needs and contribute to our data infrastructure roadmap. • Gain hands-on exposure to machine learning workflows - from data preparation and feature engineering to model training - with room to grow deeper as the team and your skills evolve.

• Manage platform performance and scalability, identifying opportunities to improve reliability, efficiency, and scalability while troubleshooting and resolving system bottlenecks. • Implement and enforce role-based access control (RBAC) and data governance, security, and compliance policies across the data platform, including support for SOX controls, auditing, and access reviews. • Manage user and service account access across the data platform, including role provisioning, access requests, and lifecycle management in alignment with security and compliance requirements. • Support user access reviews (UAR) and audit processes by maintaining accurate role assignments, access controls, and supporting documentation. • Monitor and optimize Snowflake usage and costs, including warehouse utilization, workload and query performance optimization, and data lifecycle and storage optimization. • Support and monitor Snowflake AI capabilities, including Snowflake Intelligence and agent-based workflows, ensuring appropriate governance, performance, and cost controls are in place as these features are adopted. • Collaborate with Data Engineering, Data Analytics, and AI/ML teams to ensure the data platform supports and scales with their roadmap initiatives and the organization’s evolving data needs. • Contribute to documentation, standards, and operational best practices for platform usage and governance. • Support data platform operations, including incident response and ongoing system reliability.