The Senior Data Analytics Engineer is responsible for designing, developing, and operating governed analytics, semantic models, reusable business metrics, and AI-ready analytical assets. This role combines analytics engineering, semantic modeling, business intelligence, data quality, and practical AI enablement to deliver trusted data products that support reporting, decision-making, self-service analytics, and approved AI use cases.
 

The position partners closely with Data Engineering, Data Governance, Data Science, business analysts, and application teams to translate business definitions, source-system context, and analytical requirements into scalable semantic models and governed consumption layers. The ideal candidate is a hands-on senior individual contributor with deep SQL, BI, semantic modeling, and analytics engineering expertise, along with working knowledge of data science, knowledge graphs, retrieval-augmented generation, and AI-ready data patterns.

What you’ll do…

Analytics Engineering, Semantic Models & Data Products

• Design, build, test, and maintain semantic models, dimensional models, curated datasets, measures, KPIs, hierarchies, and reusable business logic.
• Develop enterprise analytics solutions using SQL, Power BI, Microsoft Fabric, Databricks, and approved cloud services.
• Consume governed Gold-layer data and work with Data Engineers to resolve modeling, quality, performance, and integration issues.
• Optimize semantic models and analytical queries for usability, scalability, refresh performance, and secure access.
• Support dashboard developers, analysts, and self-service users with well-documented analytical assets.
   

Applied Data Science & AI Readiness

• Apply working knowledge of statistical methods, machine learning concepts, and AI patterns to design analytics assets that can support downstream data science and AI use cases.
• Partner with AI and Engineering teams to understand modeling, feature, evaluation, and retrieval requirements and translate them into reliable analytical datasets and reusable data products.
• Develop curated feature-ready datasets, dimensional models, and semantic structures that support forecasting, segmentation, classification, anomaly detection, and other approved analytical use cases.
• Support generative AI and RAG solutions by preparing high-quality business definitions, metadata, retrieval-ready content, embeddings inputs, and governed analytical context.
• Contribute to knowledge graph and ontology-aligned data structures that connect business concepts, metrics, entities, relationships, source systems, and governed data assets.
• Perform exploratory analysis, profiling, reconciliation, and validation to confirm that analytics and AI-ready assets are accurate, explainable, and fit for business consumption.
• Communicate findings, data limitations, modeling assumptions, and recommended actions to business, technical, and governance stakeholders.

AI Engineering & MLOps Delivery

• Build and maintain deployment pipelines for machine learning and generative AI workloads using version control, automated testing, and CI/CD practices.
• Support experiment tracking, model registration, release management, batch or real-time inference, monitoring, drift detection, and operational troubleshooting.
• Develop reusable feature datasets, evaluation datasets, retrieval pipelines, embeddings, and vectorized knowledge assets for approved AI use cases.
• Implement automated tests for data, semantic models, model artifacts, prompts, retrieval quality, and production workflows.
• Collaborate with Data Engineering teams to productionize machine learning solutions through scalable data pipelines and MLOps frameworks.
• Partner with Data Scientists and platform teams to improve reliability, observability, security, responsible AI controls, and cost management.

Governance, Quality & Documentation

• Document business definitions, calculations, data sources, model dependencies, ownership, and operational procedures.
• Partner with Data Governance on glossary alignment, metadata, lineage, classification, access controls, and quality expectations.
• Perform data profiling, reconciliation, root-cause analysis, and issue remediation across analytics and AI workflows.
• Ensure analytical and AI assets follow established architecture, privacy, security, and responsible AI standards.

Business Partnership & Team Contribution

• Translate business questions and AI use-case requirements into practical technical designs and delivery plans.
• Collaborate with cross-functional teams across Finance, GTM, Product, Customer, People, and other enterprise domains.
• Present technical findings, analytical results, risks, and recommendations clearly to business and technical audiences.
• Mentor analysts and engineers on semantic modeling, SQL, testing, deployment, and operational best practices.
• Contribute reusable code, patterns, documentation, and lessons learned to the Analytics & Semantics capability.
   

Who you are…

• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Analytics, Data Science, Mathematics, Statistics, or related quantitative or technical field.
• Minimum of 5 years of professional experience in analytics engineering, business intelligence, semantic modeling, data engineering, data analytics, or related discipline.
• Advanced SQL experience, including complex query development, performance optimization, data profiling, reconciliation, and analysis across large-scale enterprise datasets.
• Experience designing and maintaining semantic models, dimensional models, metrics layers, KPIs, hierarchies, relationships, reusable calculations, and governed business logic.
• Hands-on experience developing analytics solutions using Power BI, Microsoft Fabric, Databricks, Spark, Python, or comparable modern data and analytics platforms.
• Experience building curated analytical datasets, feature-ready data assets, and governed consumption layers that support reporting, self-service analytics, data science, and AI use cases.
• Working knowledge of statistical analysis, machine learning concepts, feature engineering, model evaluation, and common data science use cases such as forecasting, segmentation, classification, anomaly detection, and recommendation.
• Working knowledge of generative AI and retrieval patterns, including large language models, embeddings, vector search, retrieval-augmented generation, prompt evaluation, and AI agents.
• Familiarity with knowledge graph, ontology, taxonomy, metadata, lineage, glossary, and entity-relationship concepts used to connect business meaning with governed data assets.
• Experience implementing data quality checks, validation routines, testing practices, documentation standards, source-to-target mapping, and operational controls for analytics and semantic assets.
• Experience partnering with cross-functional stakeholders to translate business definitions, source-system context, reporting needs, and AI requirements into scalable technical designs.
• Strong written and verbal communication skills, with the ability to explain metrics, data lineage, data quality findings, analytical logic, risks, and recommendations to business, technical, and leadership audiences.
   

Preferred Skills & Experience

• Experience with Power BI semantic models, DAX, Microsoft Fabric, Databricks SQL, Unity Catalog, Microsoft Purview, or comparable data governance and analytics platforms.
• Experience developing business intelligence dashboards and analytics products; Power BI experience is preferred, and experience with Tableau, Qlik, or comparable tools is also acceptable.
• Knowledge of enterprise business applications such as Salesforce, NetSuite, Marketo, Workday, or comparable CRM, ERP, marketing automation, and HR systems is strongly preferred.
• Experience supporting knowledge graph, ontology, taxonomy, business glossary, entity resolution, or semantic layer initiatives.
• Experience preparing retrieval-ready data, metadata, embeddings inputs, vector search assets, or governed context for RAG and enterprise AI solutions.
• Experience working in an Agile product or platform delivery model with geographically distributed teams.

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