Lead design and delivery of Microsoft Fabric-based data pipelines, Lakehouse data models, and ETL in Python. Partner with Finance to translate requirements, ensure data quality, enforce engineering standards, mentor engineers, and produce analytics and reporting (Power BI/Databricks) to support forecasting and profitability analysis.
Work Schedule
Standard (Mon-Fri)Environmental Conditions
OfficeJob Description
When you join us at Thermo Fisher Scientific, you’ll be part of an inquisitive team that shares your passion for exploration and discovery. With revenues exceeding $40 billion and the industry's largest investment in R&D, we provide our people the resources and chances to create significant contributions to the world.
If you are passionate about the intersection of finance, data solutions, data science, analytics, and data visualization, this is the role for you. These areas come together to tell a story and drive decision-making within a world-leading life science company.
As a member of the Finance Digital Transformation group, the Data & Analytics Engineer will collaborate across various divisions and functions. They will build, develop, and maintain analytics and data solutions for customers with diverse technical backgrounds. Their work aims to generate data insights that improve efficiency, productivity, and revenue.
Key Responsibilities:
- 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%
Top Skills
Python,Sql,Microsoft Fabric,Power Bi,Databricks,Tableau,Sas,Apache Spark,Pandas,Pyspark,Sqlalchemy,Lakehouse Architecture,Fabric Data Warehouse
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