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 Data Analyst Analytics Engineering & AI to support the development of modern enterprise analytics capabilities and help accelerate the evolution of digital analytics toward AI-supported insights.

Reporting to the Senior Manager Analytics & Insights the Data Analyst will work at the intersection of business analytics data visualization analytics engineering data modeling and AI-enabled analytics.

This role will partner with business stakeholders senior analysts data engineers architects and product teams to transform business questions into trusted datasets analytical models dashboards reports and actionable insights.

The Data Analyst will also contribute to the development of an AI-ready analytics foundation by helping organize business metrics metadata data relationships definitions and analytical datasets so they can be consistently used across dashboards self-service analytics conversational analytics and emerging AI applications.

The ideal candidate is analytically curious technically capable comfortable working with complex data and interested in developing skills across analytics engineering semantic modeling and AI-enabled analytics.

• Partner with business stakeholders and senior analytics team members to understand business questions reporting requirements KPIs and analytical needs.
• Analyze large and complex datasets to identify trends patterns opportunities anomalies and drivers of business performance.
• Develop recurring and ad hoc analyses supporting commercial customer digital operational and strategic initiatives.
• Build and maintain dashboards reports scorecards and visualizations that provide clear and actionable insights to business stakeholders.
• Write and maintain SQL queries and data transformations used to create analytical datasets and support reporting and analysis.
• Assist in developing reusable analytical datasets and data models that improve consistency and reduce duplicated reporting logic.
• Support the development and maintenance of a trusted semantic analytics layer including business definitions KPIs dimensions measures hierarchies and relationships.
• Work with senior analysts and business stakeholders to document and validate metric definitions and ensure consistent interpretation across analytical products.
• Perform data profiling validation reconciliation and quality checks to identify inconsistencies missing data or unexpected results.
• Investigate data-quality and reporting issues and partner with analytics engineering and data engineering teams to identify root causes.
• Support the creation and maintenance of documentation covering data sources transformations metric calculations business rules assumptions and analytical logic.
• Assist with ETL/ELT and analytics engineering activities including data transformations testing validation and maintenance of curated analytical datasets.
• Use Python or similar analytical technologies for data preparation automation exploratory analysis validation and analytical workflows as appropriate.
• Support customer product digital marketing commercial and operational analytics initiatives through data exploration and performance measurement.
• Assist with experimentation segmentation funnel analysis customer journey analysis KPI tracking forecasting and other analytical methodologies as needed.
• Work with senior team members to identify opportunities to automate manual reports repetitive analysis and data-preparation processes.
• Support the transition from traditional reporting toward more self-service proactive and AI-enabled analytical experiences.
• Participate in the development and testing of conversational analytics natural-language querying AI-generated insights semantic search and other emerging analytical capabilities.
• Validate AI-generated analytical outputs by comparing responses against trusted datasets established metrics source systems and documented business rules.
• Help organize metadata business definitions and semantic context so AI-enabled analytics solutions can more accurately interpret enterprise data.
• Collaborate with data engineering architecture security product and governance teams to ensure analytical solutions follow organizational standards.
• Participate in peer reviews testing documentation and continuous improvement of analytics products and processes.
• Communicate analytical findings clearly to both technical and non-technical stakeholders using effective visualizations summaries and recommendations.
• Continuously develop knowledge of modern analytics data engineering semantic technologies cloud data platforms generative AI and emerging analytical practices.

• Bachelors degree in Computer Science Data Science Engineering Information Systems Business Analytics Statistics Mathematics Economics or a related quantitative discipline.
• Equivalent combination of education and relevant professional experience may be considered.
• Advanced degree or relevant professional certifications are beneficial but not required.
• 25 years of experience in data analytics business intelligence analytics engineering data engineering data science or a related discipline.
• Experience working with structured datasets and translating business questions into analytical outputs.
• Experience developing dashboards reports analyses or analytical datasets in a business environment.
• Experience working with relational databases cloud data warehouses or enterprise analytical platforms.
• Exposure to data modeling dimensional modeling semantic models metrics layers or curated analytical datasets is preferred.
• Experience with cloud-based data platforms such as Snowflake Teradata Hadoop AWS Azure Google Cloud Platform or similar technologies is beneficial.
• Experience with digital analytics technologies such as Google Analytics or Adobe Analytics is beneficial.
• Exposure to enterprise systems such as SAP ERP CRM digital commerce marketing customer or operational platforms is preferred.
• Exposure to generative AI conversational analytics natural-language querying semantic search machine learning or AI-enabled analytics is beneficial

• Strong SQL skills including joins aggregations subqueries common table expressions window functions and analytical querying.
• Working knowledge of Python or another analytical programming language for data manipulation automation analysis or validation.
• Experience with visualization and business intelligence technologies such as Tableau Power BI or similar tools.
• Understanding of data structures relational databases analytical datasets and basic data modeling concepts.
• Familiarity with ETL/ELT concepts and modern data-transformation workflows.
• Understanding of data-quality principles including validation reconciliation completeness consistency and accuracy.
• Familiarity with version control documentation testing or collaborative software/data development practices is beneficial.
• Basic understanding of cloud data architectures and modern data warehouse technologies.
• Interest in or exposure to generative AI large language models semantic search embeddings knowledge models or AI agents is preferred.
• Ability to understand how business definitions metadata data relationships and metric calculations affect analytics and AI-generated insights.

The Data Analyst will have the opportunity to develop capabilities beyond traditional reporting and contribute to the semantic and analytical foundation supporting next-generation analytics.

Key responsibilities may include:

• Supporting the definition and maintenance of reusable business metrics and KPIs.
• Helping document business terminology and map business concepts to enterprise data.
• Assisting in defining relationships between customers products channels transactions campaigns digital interactions and other business entities.
• Building analytical datasets that can be reused across dashboards reports analyses and AI-enabled applications.
• Helping identify differences between business terminology and source-system definitions.
• Contributing metadata definitions descriptions and contextual information that improve self-service and AI-enabled analytics.
• Testing natural-language questions against analytical datasets and identifying cases where AI-generated responses are inaccurate or ambiguous.
• Validating AI-generated analytical responses against trusted data and established business definitions.
• Supporting data and semantic quality controls that improve accuracy consistency explainability and trust.
• Developing an understanding of how governed enterprise data can support AI agents and conversational analytical experiences.

• Strong analytical and problem-solving skills.
• Curiosity and willingness to investigate unfamiliar data and business problems.
• Ability to translate data into clear observations and business insights.
• Strong attention to detail and commitment to analytical accuracy.
• Ability to communicate findings effectively to technical and non-technical audiences.
• Ability to work collaboratively across analytics engineering product technology and business teams.
• Ability to manage multiple assignments and priorities in a fast-paced environment.
• Willingness to ask questions challenge assumptions constructively and seek deeper understanding of business problems.
• Strong documentation and organizational skills.
• Commitment to continuous learning and development across analytics data engineering and AI technologies.