About The Role
We are seeking a BI Architect to advise the BI Lead of a global CPG organization and architect an intelligent, scalable Business Intelligence ecosystem. This includes an enterprise-wide KPI dashboard suite augmented by a GenAI-driven natural language interface for insight discovery.
The ideal candidate will be responsible for end-to-end architecture : from scalable data models and dashboards to a conversational interface powered by Retrieval-Augmented Generation (RAG) and/or Knowledge Graphs. The solution must synthesize internal BI data with external (web-scraped and competitor) data to deliver intelligent, context-rich insights.
Key Responsibilities
- Architect BI Stack: Design and oversee a scalable and performant BI platform that serves as a single source of truth for key business metrics across functions (Sales, Marketing, Supply Chain, Finance, etc.).
- Advise BI Lead: Act as a technical thought partner to the BI Lead, aligning architecture decisions with long-term strategy and business priorities.
- Design GenAI Layer: Architect a GenAI-powered natural language interface on top of BI dashboards to allow business users to query KPIs, trends, and anomalies conversationally.
- RAG/Graph Approach: Select and implement appropriate architectures (e.g., RAG using vector stores, Knowledge Graphs) to support intelligent, context-aware insights.
- External Data Integration: Build mechanisms to ingest and structure data from public sources (e.g., competitor websites, industry reports, social sentiment) to augment internal insights.
- Security & Governance: Ensure all layers (BI + GenAI) adhere to enterprise data governance, security, and compliance standards.
- Cross-functional Collaboration: Work closely with Data Engineering, Analytics, and Product teams to ensure seamless integration and 79 years of experience in BI architecture and analytics platforms, with at least 2 years working on GenAI, RAG, or LLM-based solutions.
- Strong expertise in BI tools (e.g., Power BI, Tableau, Looker) and data modeling.
- Experience with GenAI frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel) and vector databases (e.g., Pinecone, FAISS, Weaviate).
- Knowledge of graph-based data models and tools (e.g., Neo4j, RDF, SPARQL) is a plus.
- Proficiency in Python or relevant scripting language for pipeline orchestration and AI integration.
- Familiarity with web scraping and structuring external/third-party datasets.
- Prior experience in CPG domain or large-scale KPI dashboarding preferred.
(ref:hirist.tech)
