Data Analytics Engineer

Role overview

Build and maintain trusted analytical datasets, semantic models, governed metrics, and reusable reporting assets for enterprise reporting and self-service analysis. You will translate business definitions into consistent data structures and measures, working with domain teams, data engineering, governance, and platform partners. The role also helps prepare reliable, well-documented data for predictive and generative AI use cases.

Responsibilities

- Design, test, and maintain semantic models in Microsoft Fabric and Power BI, including dimensional structures, measures, KPIs, hierarchies, and reusable components. - Convert reporting needs and business definitions into governed models, and improve relationships, DAX calculations, refresh approaches, aggregations, and query performance. - Create curated datasets, analytical views, SQL transformations, validation routines, and reconciliation controls with data engineering partners. - Support report and model modernization, participating in design reviews, testing, deployment, incident resolution, and production support. - Document definitions, ownership, metadata, lineage, data quality expectations, and approved usage; align assets with governance, access, and certification practices. - Work with business stakeholders to explain metric logic and data limitations, and identify ways to reduce duplication and make analytics easier to use.

Requirements

- At least two years of experience in analytics engineering, business intelligence, semantic modeling, data analysis, or a related discipline. - Strong SQL skills and practical experience with dimensional modeling and analytical data structures. - Hands-on experience with Power BI and/or Microsoft Fabric semantic modeling. - Ability to translate business concepts and reporting requirements into clear, reusable models and measures. - Care with documentation, data quality, performance, and supportability, along with the ability to explain technical concepts to nontechnical partners.

Nice to have

Experience in enterprise Finance, go-to-market, Product, Customer, or Workforce analytics. Familiarity with preparing trusted datasets and metrics for predictive analytics, generative AI, copilots, agents, or retrieval use cases is useful.

Benefits and work setup

The role offers visibility across business domains and opportunities to work with modern analytics, cloud, and AI technologies. The source also describes a flexible environment that supports well-being and continued learning.