We are hiring a Reporting Engineer to join our Business Intelligence & Reporting team and help build, maintain, and scale the reporting and analytics layer of the AtlasFive® platform. This is a hands-on role focused on Power BI report development, Microsoft Fabric-based data pipelines, and SQL Server-backed data models, all running on Azure infrastructure. The ideal candidate is comfortable working across the reporting stack — from writing efficient SQL and Python to building performant Power BI datasets and Fabric pipelines — and is diligent about data accuracy, performance, and documentation.

Power BI & Reporting Development Design, build, and maintain Power BI reports, dashboards, and data models to support business and client reporting needs Develop and optimize Power BI datasets, dataflows, and semantic models (DAX) for performance and scalability Implement row-level security (RLS) and access controls in Power BI reports Manage Power BI Service — workspaces, gateways, refresh schedules, and capacity allocation Collaborate with business stakeholders to translate reporting requirements into effective visualizations Microsoft Fabric & Data Engineering Build and maintain data pipelines, dataflows, and lakehouse artifacts within microsoft Fabric Develop notebooks and pipelines using Python (and PySpark where applicable) for data transformation and processing Support Fabric capacity monitoring, workspace management, and performance tuning

3 – 4 years of hands-on experience in BI/reporting engineering, data analytics, or related roles Strong hands-on experience with Power BI reporting — report/dashboard development, data modeling, DAX Experience with Microsoft Fabric (Lakehouse, Dataflows, Pipelines, Notebooks) Proficiency in Python for data processing and automation Strong SQL Server skills — writing and optimizing queries, stored procedures, and data models Working knowledge of Azure services supporting data and reporting workloads Technical Skills Reporting: Power BI (Desktop & Service) — data modeling, DAX, RLS, workspace/gateway management Data Platform: Microsoft Fabric — Lakehouse, Dataflows Gen2, Data Pipelines, Notebooks Programming: Python (mandatory) Database: SQL Server — T-SQL, stored procedures, performance tuning Cloud: Azure — Azure SQL, Storage, Data Factory / Synapse, Key Vault, Monitor