Must Have Technical/Functional Skills
• 8+ years of experience in software engineering, analytics, business intelligence, or AI application development
• Hands-on experience with Microsoft Fabric, including OneLake, Lakehouse or Warehouse, Power BI semantic models, and Power BI Embedded
• Working knowledge of Fabric IQ concepts, including ontologies, business entities, relationships, graph-based context, and agent-ready semantic layers.
• Strong experience developing modern web applications using React 18.x or 19.x, TypeScript, JavaScript, reusable UI components, and REST APIs.
• Experience building AI-led reports and AI-embedded analytics, including natural-language interaction, automated narratives, anomaly or trend explanations, recommendations, and conversational analytics.
• Hands-on experience with Azure OpenAI or comparable large language model services, prompt engineering, retrieval-augmented generation, semantic search, and AI evaluation.
• Strong understanding of report discovery, persona and use-case analysis, KPI definition, data lineage, semantic modeling, and report rationalization.
• Ability to rapidly prototype 2 to 3 high-value reports and convert discovery findings into epics, features, user stories, acceptance criteria, estimates, dependencies, and a prioritized backlog.
• Experience defining reusable architecture patterns, report templates, React components, prompt libraries, semantic models, and governance controls for enterprise-scale adoption.
• Strong SQL skills; proficiency in Python or C# is preferred. Experience integrating structured and unstructured enterprise data is desirable.
• Excellent facilitation, stakeholder management, communication, and storytelling skills for both technical and executive audiences.
Roles & Responsibilities
• Lead discovery workshops with business, product, data, UX, security, and technology stakeholders to understand current reports, decisions supported, user journeys, pain points, KPIs, data sources, and regulatoryconstraints.
• Assess the existing reporting landscape and classify reports for modernization, consolidation, redesign, reuse, or retirement using agreed business-value, complexity, risk, and usage criteria.
• Select and develop an initial set of 2 to 3 representative AI-powered report prototypes that demonstrate measurable business value and establish reusable implementation patterns.
• Design AI-led reports that proactively surface insights, drivers, trends, exceptions, contextual narratives, and recommended next actions instead of presenting static metrics alone.
• Build AI-embedded report experiences in React and Power BI Embedded, including conversational interfaces, natural-language exploration, guided analysis, explainable insights, and role-aware experiences.
• Develop and align Fabric IQ ontologies, Power BI semantic models, business definitions, relationships, rules, and actions so report and AI agents use consistent enterprise context.
• Integrate enterprise data from Microsoft Fabric, APIs, lakehouse or warehouse platforms, operational systems, documents, and approved knowledge sources while maintaining security and lineage.
• Validate prototypes with end users through demonstrations and structured feedback; document business outcomes, functional gaps, technical constraints, adoption considerations,and lessons learned.
• Translate discovery and prototype findings into a delivery-ready backlog containing epics, capabilities, features, user stories, acceptance criteria, technical enablers, non-functional requirements, dependencies, risks, and prioritization rationale.
• Define the roadmap and scalable delivery approach for expanding from the initial prototypes to an approximately 3,000-report estate, including waves, report archetypes, reusable accelerators, automation opportunities, qualitygates, and governance.
• Establish development standards for React components, embedded analytics, prompts, semantic models, AI evaluation, accessibility, observability, testing, deployment, and responsible AI.
• Collaborate with product owners and delivery teams on planning, estimation, release sequencing, sprint execution, demos, documentation,and knowledge transfer.
• Measure outcomes such as adoption, decision-cycle improvement, report consolidation, insight quality, response accuracy, performance, and reuse of common assets.