Senior Manager Data Science and Analytics Engineering

Work Schedule Standard (Mon-Fri) Environmental Conditions Office Job Description When you join Thermo Fisher Scientific, you become part of a team that is committed to enabling discovery, improving outcomes, and driving meaningful impact through data. Within our Finance Digital Team, we are redefining how data is structured, accessed, and used to power decision-making across a complex, global organization. This role sits at the intersection of data engineering, analytics, and finance. It is focused on building scalable data models, enabling advanced analytics, and shaping how data is used—not just building pipelines. You will work closely with data scientists, analysts, and finance leaders to design flexible, analytics-ready data structures, support data science initiatives and elevate the capabilities of the broader team.

Key Responsibilities:

Own the design of analytical and semantic data models that support financial reporting, advanced analytics, and future capabilities Lead the development of scalable, analytics-ready data products Lead efforts and partner with data scientists and analysts to enable experimentation, feature engineering, and advanced analytics by shaping how data is modeled and accessed. Translate complex business problems into flexible data solutions, working closely with Finance leadership to define requirements and deliver actionable insights. Remain hands-on in Python, SQL, and Databricks/Spark environments, prototyping solutions and guiding technical implementation. Lead and develop a cross-functional team of data engineers, analysts, and data scientists, building capabilities in modern data frameworks and analytical thinking. Evaluate and introduce new technologies and approaches across Databricks, Microsoft Fabric, AI/ML, and data orchestration tools to continuously improve team effectiveness.

Requirements/Qualifications:

8+ years of experience in data engineering, analytics engineering, data science, or related roles with increasing technical scope and ownership 2-5+ years of experience leading technical teams, with a track record of developing talent across data engineering, analytics, and/or data science Strong hands-on expertise in Python and SQL, with the ability to prototype, debug, and guide implementation directly Deep experience working in modern data platforms such as Databricks (preferred), Microsoft Fabric, or similar Spark/lakehouse environments Proven experience designing and implementing scalable data models and analytics-ready datasets (not just data pipelines), including support for BI and advanced analytics use cases Experience working closely with data scientists and analysts, enabling workflows such as experimentation, feature engineering, and advanced analytics Strong understanding of data modeling concepts (dimensional modeling, semantic layers, data products) and how they support business decision-making Experience translating ambiguous business problems into flexible, scalable data solutions Ability to operate effectively in evolving architectures and toolsets, balancing speed, flexibility, and governance (e.g., Databricks, Fabric, Unity Catalog) Familiarity with BI and visualization tools such as Power BI or Tableau Preferred Qualifications Experience with semantic modeling or governed data layers (e.g., Unity Catalog, dbt, or similar) Background supporting finance, ERP, or operational data domains Exposure to AI/ML workflows and supporting infrastructure Experience with modern data practices such as version-controlled transformations, testing, and modular data design Familiarity with Agile or product-oriented delivery models