Senior Manager Data Engineering / Data ScienceThermo Fisher Scientific • Raleigh, North Carolina, United States of America
Senior Manager Data Engineering / Data Science
Thermo Fisher Scientific • Raleigh, North Carolina, United States of America
[job_card.variable_days_ago]
[job_preview.job_type]
[job_card.full_time]
[job_card.job_description]
:
Lead the architecture and implementation of data pipelines and Microsoft Fabric-based data models to enable unified financial reporting and analytics.
Build, review, and optimize ETL processes developed in Python, ensuring efficient data ingestion, transformation, and quality across diverse data sources (ERP, financial systems, operational platforms).
Partner with Finance leadership to define business requirements, translate them into robust technical builds, and deliver actionable insights for decision-making.
Establish and enforce data engineering standards, including coding procedures, documentation, version control, and automated testing for ETL pipelines.
Develop, test, and deploy scalable data models using Lakehouse architectures and Fabric Data Warehouses to support forecasting, planning, and profitability analysis.
Provide technical leadership and mentorship to data engineers and analysts, encouraging skill growth in Fabric, Power BI, and modern Python data frameworks (Pandas, PySpark, SQLAlchemy).
Collaborate multi-functionally with Finance, IT, and Data Governance teams to ensure alignment with enterprise data architecture and security policies.
Keep up-to-date with developments in Microsoft Fabric, Power BI, Python, and data orchestration tools, and suggest their strategic implementation.
Requirements/Qualifications:
Bachelor’s degree or equivalent experience in a quantitative field, such as Statistics, Computer Science, Mathematics, Data Science or a related field; master’s or equivalent experience preferred
4+ years’ experience in a data engineer or data science role with progressive responsibilities and scope
Experience building models and analyzing large, complex data sets yielding opportunities for revenue and/or process improvement within an organization
Technical proficiency in Python and SQL
Proficiency in data engineering and reporting platforms such as Databricks, Power Bi, Tableau, SAS Analytics
Knowledge of Agile/Scrum methodology is a plus
Hands-on experience with distributed computational framework, such as Spark
Minimal travel required <10%
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