Senior Data Analytics Engineer

Role Overview

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.

What You Will Do

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.

Why It Might Be a Fit

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.

Requirements

• 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.

Benefits

• Dental insurance
• Vision insurance
• Health insurance
• Retirement plan
• Paid time off
• Flexible work arrangements
• Professional development opportunities
• Equal opportunity workplace
• Affirmative action employer

Tagged as: Data Analysis