Senior Data Analyst – Analytics Engineering & AI

Work Schedule

Environmental Conditions

Job Description

At Thermo Fisher Scientific our mission is to enable our customers to make the world healthier cleaner and safer.

We are seeking a Senior Data Analyst Analytics Engineering & AI to help advance our enterprise analytics capabilities and accelerate the modernization of digital analytics toward AI-supported insights.

Reporting to the Director or Senior Manager of Analytics AI & Insights this role will serve as a senior individual contributor at the intersection of business analytics analytics engineering data engineering semantic modeling and AI-enabled analytics.

The Senior Data Analyst will partner closely with business stakeholders data engineers architects product teams and technology partners to transform complex business questions into trusted data products scalable analytical solutions reusable semantic models and actionable insights.

A key focus of the role will be creating an AI-ready analytics foundationensuring that enterprise data business metrics metadata relationships and definitions are structured so they can be consistently consumed by dashboards analysts conversational analytics platforms AI agents and other emerging analytical experiences.

The ideal candidate combines strong analytical thinking and business acumen with hands-on SQL data modeling analytics engineering visualization and modern AI/data capabilities.

• Translate complex business questions into analytical requirements data models metrics dashboards data products and actionable recommendations.
• Develop advanced analyses that identify trends opportunities root causes customer behaviors operational drivers and areas for performance improvement.
• Design and maintain scalable analytical datasets and reusable data models supporting commercial customer digital operational and strategic decision-making.
• Build and maintain a trusted semantic analytics layer that standardizes business entities dimensions measures KPIs relationships definitions and calculation logic across analytical applications.
• Partner with business stakeholders and data owners to establish consistent definitions for key enterprise metrics and ensure analytics products provide a common interpretation of business performance.
• Structure data metadata business definitions lineage relationships and contextual information to improve the ability of AI and conversational analytics solutions to accurately understand and reason across enterprise information.
• Support the development of AI-enabled analytics experiences including conversational analytics natural-language querying AI-generated insights intelligent search and agentic analytics workflows.
• Evaluate and improve the accuracy of AI-generated analytical responses by validating metric calculations semantic context data mappings source data business rules and analytical outputs.
• Partner with data engineering teams to design and develop reliable ETL/ELT pipelines analytical data transformations curated datasets and reusable data products.
• Develop complex SQL transformations and use Python or similar technologies to automate analytical processes perform advanced analysis validate data and improve analytical workflows.
• Perform data profiling validation reconciliation and root-cause analysis to identify data-quality issues and improve confidence in enterprise analytics.
• Develop dashboards scorecards visualizations and self-service analytical products that provide stakeholders with meaningful and actionable insights.
• Support experimentation KPI measurement customer journey analysis forecasting segmentation attribution and performance measurement as appropriate.
• Collaborate with data architecture engineering security product and governance teams to ensure analytical solutions follow enterprise standards for architecture security privacy quality and governance.
• Document analytical models semantic definitions transformations business rules data lineage assumptions and metric calculations to improve transparency and reuse.
• Identify opportunities to simplify and automate existing reporting and analytical processes while reducing manual data preparation and duplicated business logic.
• Contribute to the modernization of digital analytics by helping transition from traditional dashboard-centric reporting toward AI-supported proactive and conversational insights.
• Research and evaluate emerging capabilities in analytics engineering semantic technologies generative AI machine learning and modern data platforms that could improve enterprise decision-making.
• Serve as a subject-matter expert and trusted analytical partner to business and technology stakeholders clearly communicating analytical findings recommendations limitations and implications.
• Mentor analysts and other team members on analytical methodologies SQL data modeling semantic design visualization and effective use of modern analytics technologies.

• Bachelors degree in Computer Science Data Science Engineering Information Systems Business Analytics Statistics Mathematics Economics or a related quantitative discipline.
• Masters degree in a quantitative technical or business discipline preferred.
• 6 years of experience in data analytics business intelligence analytics engineering data engineering data science or a related discipline.
• Demonstrated experience translating ambiguous or complex business problems into structured analytical solutions.
• Strong experience working with enterprise-scale data environments and large complex datasets.
• Experience developing analytical data models and curated datasets for reporting analytics and downstream consumption.
• Experience working with cloud-based data warehouses or analytical platforms such as Snowflake Teradata Hadoop AWS Azure Google Cloud Platform or similar technologies.
• Experience designing or working with semantic models metrics layers dimensional models business metadata or governed analytical datasets.
• Experience supporting AI-enabled analytics conversational analytics natural-language-to-data experiences generative AI applications or semantic search is preferred.
• Experience in digital analytics platforms such as Google Analytics or Adobe Analytics is beneficial.
• Experience integrating analytical data with enterprise systems such as SAP ERP CRM digital commerce marketing customer or operational systems is preferred.

• Advanced SQL skills including complex transformations joins window functions optimization reconciliation and analytical querying.
• Proficiency with Python for analytics data manipulation automation validation or analytical application development.
• Strong understanding of data modeling including dimensional modeling fact/dimension structures analytical datasets and reusable business entities.
• Understanding of modern ETL/ELT and analytics engineering practices including transformation pipelines testing documentation version control and deployment.
• Experience with visualization and business intelligence technologies such as Tableau Power BI or similar platforms.
• Understanding of data quality data lineage metadata management governance and master/reference data concepts.
• Knowledge of APIs structured and semi-structured data cloud data architectures and modern data integration patterns.
• Familiarity with generative AI large language models retrieval-based architectures semantic search embeddings knowledge models or AI agents is preferred.
• Understanding of how metadata business terminology semantic relationships metric definitions and governed data influence the accuracy and reliability of AI-generated analytical responses.

The successful candidate should be able to operate beyond traditional reporting and help establish the semantic foundation required for the next generation of enterprise analytics.

Key capabilities include:

• Defining reusable enterprise business metrics and KPI logic.
• Modeling relationships between customers products channels transactions campaigns digital interactions and other important business entities.
• Creating analytical models that can be consumed consistently by humans BI platforms APIs and AI applications.
• Translating business terminology into structured metadata and machine-understandable definitions.
• Identifying and resolving inconsistencies between source-system terminology and enterprise business definitions.
• Designing analytical context that improves natural-language querying and AI interpretation of enterprise data.
• Testing AI-generated analytical answers against governed data and established metric definitions.
• Helping establish guardrails that ensure AI-enabled analytics respect data access governance privacy security and business rules.
• Balancing emerging AI capabilities with accuracy explainability reproducibility and trusted enterprise data.

• Strong analytical and structured problem-solving skills.
• Ability to move fluidly between business problems and technical implementation.
• Strong curiosity and ability to uncover the business meaning behind data.
• Ability to communicate complex analytical and technical concepts clearly to both technical and non-technical audiences.
• Strong stakeholder-management and consulting skills.
• Ability to independently manage multiple priorities and analytical initiatives.
• Strong attention to data quality analytical accuracy and business context.
• Ability to challenge assumptions constructively and use data to influence decisions.
• Collaborative approach to working across analytics engineering architecture product security and business teams.
• Commitment to continuous learning and adoption of emerging analytics and AI technologies.