JOB DESCRIPTION
Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Job Description
JOB SUMMARY
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 foundation —ensuring 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.
MAJOR JOB DUTIES AND RESPONSIBILITIES
- 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.
QUALIFICATIONS (Education/Training, Experience and Certifications)
- Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Business Analytics, Statistics, Mathematics, Economics, or a related quantitative discipline.
- Master's 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.
TECHNICAL SKILLS
- 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.
ANALYTICS & AI SEMANTIC ENGINEERING CAPABILITIES
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.
CORE COMPETENCIES
- 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.