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Power BI Developer

Ambition Recruitment · Central and Western District

📅 18/08/2026
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Key Responsibilities 1. AI & Fabric Data Agent Development Build & Deploy Data Agents: Design, test, and maintain Fabric-native Data Agents using Azure OpenAI Assistant APIs to enable natural language querying across Lakehouse, Warehouses, and KQL databases. Agentic Prompt Engineering: Design system prompts, few-shot examples, domain-specific instructions, and chain-of-thought instructions to ensure AI agents generate accurate, secure, and context-aware SQL/DAX queries. Guardrails & Governance: Integrate Azure AI Content Safety and Microsoft Purview policies to enforce read-only data access, security boundaries, and prevent hallucinated or unauthorized outputs. 2. Microsoft Fabric & Semantic Modelling AI-Ready Architecture: Clean and structure data in Fabric OneLake to be "AI-ready". Optimize tables, columns, and relationship naming conventions so LLMs can interpret schemas accurately. Semantic Model Design: Develop highly optimized Power BI Semantic Models with Direct Lake connectivity to provide the single source of truth for both human-facing dashboards and AI agents. Data Orchestration: Build and troubleshoot pipelines using Dataflows Gen2 and Data Factory inside Fabric to ingest diverse enterprise data. 3. BI & Dashboards Interactive Visualizations: Design and build executive-level Power BI dashboards with intuitive UX, focusing on actionable insights. Hybrid Delivery: Ensure that users can transition seamlessly between structured reports (Power BI) and conversational analytics (Fabric Data Agents). Advanced Analytics: Implement complex DAX measures, calculations, and analytical patterns to support diagnostic and predictive business questions. Required Skills & Experience Technical Stack Platform: 2+ years of hands-on experience with Microsoft Fabric (OneLake, Lakehouse, Warehouses, and Real-Time Intelligence). BI & Analytics: Strong proficiency in Power BI, advanced DAX, and semantic data modelling. AI & Prompt Engineering: Solid understanding of Large Language Models (LLMs), prompt engineering techniques , and RAG-based architectures. Data Languages: Strong SQL and Python skills for data preparation and pipeline scripting.
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In your interview, expect this: 'Walk me through the last time you had to debug something that took you more than an hour. What was it, what did you try first, and what would you do differently?' Hiring managers ask this to see if you think systematically or just try random fixes. Have a real example ready with specifics.

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