BI Manager

The role bridges data engineering, finance, operations, and business analysts, translating complex data structures into intuitive, reusable analytical building blocks.

Main Job Responsibilities: 1 BI & Semantic Layer Ownership - Own the design and evolution of Power BI semantic models aligned to a building-blocks concept (facts, dimensions, KPIs, measures) to support scalable self-service analytics. 2 Data Modeling & KPI Design - Design robust analytical models using star schemas and KPI-driven design principles.

Define canonical KPIs, measures, and calculation logic in close collaboration with Finance and Operations. 3 Power BI Engineering Excellence - Lead advanced Power BI development using Tabular Editor, calculation groups, best-practice DAX patterns, and performance optimization techniques.

4 Version Control & Content Governance - Establish and enforce version control practices for Power BI artifacts at content level (models, measures, reports), including branching, deployment pipelines, and release discipline.

5 Microsoft Fabric Environment Design - Organize and govern the Microsoft Fabric workspace structure, semantic layer, and data access patterns to support production, development, and self-service use cases.

6 Data Quality & Semantic Governance - Partner with Data Engineering and Data Owners to remediate naming inconsistencies, technical debt, and semantic drift created in 2025. Ensure alignment between physical data and business meaning.

7 Enterprise Data Cataloging - Support metadata harvesting, lineage, ownership assignment, and glossary definition to ensure BI assets are discoverable, trusted, and reusable.

Use of MS Purview 8 Self-Service Enablement - Design and manage role-based access control and Row-Level Security (RLS) in Power BI and Fabric, ensuring data privacy and compliance while enabling broad access where appropriate.

9 Real-Time & Event-Driven Analytics - Collaborate with platform and data engineering teams to support real-time reporting and dashboards powered by event-driven pipelines and streaming data.

10 Legacy BI Rationalization - Identify and decommission redundant reports, datasets, and pipelines, simplifying the BI landscape and improving trust in analytics outputs. 11 Stakeholder Engagement - Act as the primary analytics partner for business teams, translating reporting needs into scalable semantic solutions rather than bespoke dashboards.

12 BI Team Leadership - Lead and mentor BI developers and analysts, setting quality standards and fostering a culture of reusable, business-centric analytics.

Bachelor’s degree in Data Analytics, Computer Science, Engineering, Finance, or similar Technical: Power BI (semantic models, datasets, performance tuning) Advanced DAX and Tabular Editor Star schema and analytical data modeling Calculation groups and KPI frameworks Fabric workspace design and lifecycle management Metadata, lineage, and catalog integration Row-Level Security (RLS) and role-based permissions Data access governance and compliance Self-service BI enablement programs Analyst training and BI playbooks Stakeholder engagement and requirements translation Leadership & Communication: Team leadership and capability development Stakeholder management and expectation setting Change management and adoption enablement Vendor and partner coordination