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Senior Manager Data Science and Analytics Engineering

Remote in Raleigh, NC, US • Posted 60+ days ago • Updated 2 hours ago

Full Time

On-site

Fitment

Dice Job Match Score™

⭐ Evaluating experience...

Job Details

Skills

Data Structure
Financial Reporting
Leadership
Prototyping
Cross-functional Team
Analytical Skill
Orchestration
Data Engineering
Data Science
Python
SQL
Debugging
Microsoft
Apache Spark
Analytics
Use Cases
Advanced Analytics
Data Modeling
Dimensional Modeling
Decision-making
Databricks
Business Intelligence
Visualization
Microsoft Power BI
Tableau
Semantics
Modeling
Unity
Finance
Enterprise Resource Planning
Artificial Intelligence
Machine Learning (ML)
Workflow
Testing
Agile

Summary

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

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10286239
Position Id: d99a7bd493eefdfba0ff7370ead1c2fb
Posted 30+ days ago

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