Rahul Rangarao

Summary

Analytics and data platform engineer with 7+ years designing enterprise data products, cloud-native data platforms, and AI-enabled analytics across life sciences, manufacturing, retail, financial services, and supply chain. Builds modern data ecosystems on AWS, dbt, Snowflake, and Databricks, and integrates LLMs and knowledge graphs where they earn their place. Author of knowledge-worker, a published PyPI package giving AI assistants durable, provenance-backed memory: a local-first knowledge graph with graph analytics, LLM-backed ingest, and a hard provenance invariant. Generalist by nature — comfortable owning a problem from ingestion through modeling and tooling to the report on a stakeholder's desk — and experienced mentoring engineering teams and setting technical standards.

Experience

Bristol Myers Squibb · Analytics Engineer, Global Product Development and Supply (GPS)

New Brunswick, NJ · October 2023 – September 2026

Designed a batch traceability data product on a graph database (AWS Neptune), integrating LLM-extracted supplier data, ERP (SAP) data, and other enterprise systems — accelerating batch release by 8% while strengthening raw-material traceability for manufacturing investigations.
Delivered an end-to-end data product, from requirements gathering through implementation, covering 800+ quality and manufacturing data points for commercial CAR-T operations, contributing to an improvement in manufacturing success rates from 74% to 92%.
Architected and deployed an enterprise self-service analytics platform using dbt, AWS Glue, Databricks, and CI/CD, enabling the organization's Unified Data Model (UDM) initiative and empowering 50+ engineers to deliver 400+ production-grade data products on a Medallion architecture.
Developed CXO-level KPI dashboards and automated Python reporting that standardized enterprise KPIs and accelerated decision-making across CAR-T (cell therapy) operations.
Lead and mentor a team of five data developers, establishing engineering standards through code review practices, Confluence documentation, onboarding guides, user journeys, and access matrices.
Partner with engineering, manufacturing, analytics, and business leaders to translate operational requirements into scalable data products, and design reusable analytics artifacts that improve reliability and long-term maintainability.

Mu Sigma Inc. · Senior Data Consultant

Bengaluru, India · July 2018 – March 2022

Delivered advanced analytics, business intelligence, and data consulting for Fortune 100 clients across retail, financial services, healthcare, and supply chain.
Managed and mentored a team of six analysts delivering more than 30 analytical requests per week for a Fortune 50 healthcare client's customer analytics function.
Developed enterprise Power BI and Tableau dashboards enabling marketing and sales teams to improve customer acquisition and retention.
Enhanced an enterprise risk prediction model for a fintech client, enabling proactive account retention that preserved approximately $500K in recurring revenue.
Designed an automated report rationalization process using intelligent string-matching, consolidating 70+ reports into seven standardized reports to support a Snowflake migration.
Engineered scalable analytics workflows supporting inventory planning, reporting modernization, and enterprise data transformation.
Served as a trusted consultant to Fortune 500 clients, expanding engagement and generating approximately $27K in additional revenue.

Open Source

knowledge-worker · Author & Maintainer

Local-first knowledge graph for durable, provenance-backed AI memory · PyPI · GitHub · May 2026 – present

Designed a provenance-first knowledge graph architecture that validates every document before ingestion, enforced by deterministic validation, improving data trust, traceability, and explainability in AI-driven applications.
Built the full graph lifecycle: ingestion, query, path-finding, review/merge, context export, semantic inspection, and an offline HTML graph visualizer.
Implemented advanced graph analytics including PageRank, betweenness, k-core, community detection, semantic similarity scoring, and relationship analysis.
Built append-only graph storage supporting JSON-LD, RDF, and Turtle for interoperable knowledge exchange; zero runtime dependencies in the core, with optional LLM backends (Anthropic, OpenAI, Ollama).
Established a 9-benchmark public test suite guarding provenance, recall, privacy boundaries, and context compactness; GitHub Actions CI across Python 3.10–3.13.
Published to PyPI (v0.8.2); 500+ downloads/month and adopted for real cross-session AI workflows.

Also: kompress-ultra — context-management middleware experiments for AI agent frameworks (live-data TUI dashboards, knowledge-graph visualizations, Pages deploy pipeline). AG-UI protocol — open protocol work for agent/UI interoperability.

Technical Skills

Programming: Python, SQL, JavaScript (React)
Analytics & Data Engineering: dbt, AWS Glue, Dagster, Snowflake, Databricks, ETL/ELT, data modeling, data warehousing, Medallion architecture, Unified Data Model, analytics engineering, enterprise reporting, data product development
AI & Machine Learning: LLMs, LLM orchestration, prompt engineering, retrieval-augmented generation (RAG), AI context management, knowledge graphs, graph analytics, machine learning, hypothesis & A/B testing
Knowledge & Semantics: AWS Neptune, JSON-LD, RDF, Turtle, linked data
Cloud & DevOps: AWS (Lambda, Glue, EC2, Neptune, CloudFormation, CodeBuild), Git, GitHub, GitHub Actions, CI/CD
Visualization & BI: Tableau, Power BI
Software Engineering: API development, software architecture, open source development, technical documentation, benchmark testing, version control

Summary

Experience

Bristol Myers Squibb · Analytics Engineer, Global Product Development and Supply (GPS)

Mu Sigma Inc. · Senior Data Consultant

Open Source

knowledge-worker · Author & Maintainer

Technical Skills

Education

Awards

Certifications

Additional

Contact