Data & AI Consultant - Analytics Engineer

Krishna Maran

678-***-**** **************@*****.*** LinkedIn GitHub

Education

Georgia Institute of Technology Atlanta, GA

BS Industrial Engineering, MS Analytics Aug. 2020 – May 2025 Experience

Data & AI Consultant July 2025 – Present

NTT Data (Consults with Microsoft, Amazon, Salesforce, Nvidia) Remote

• Streamlined sensitive data triage by 70% by building a FastAPI and LangGraph agent with async scan jobs that flagged PII, legal contracts, and other high risk files for human review

• Eliminated a recurring analyst review cycle by building a metadata validation pipeline that parsed Power BI semantic models and auto-flagged Critical Data Elements for governance compliance

• Reduced recruiter search time by 50% across 60,000+ talent records by designing and deploying a Snowflake Cortex search platform with an LLM-powered natural language interface Data Analyst (Contract) June 2024 – June 2025

Westhill Global Atlanta, GA

• Trimmed stakeholder data access latency from hours to under 1 minute by designing 20+ Looker dashboards

(LookML + SQL) to analyze revenue trends and contractor activity

• Cut weekly reporting time by 95% by building a GCS-to-BigQuery pipeline in Python and unified LookML model that automated coupon program reporting to deliver redemption rates, revenue, and commission by partner

• Accelerated an AlloyDB to Spanner migration by deploying Cloud Function data pipelines and integrating a Vertex AI Agent Builder, which improved the front-end search bar query speed by 40% for 500+ users Operations Analyst Intern May 2023 – Aug. 2023

W.L. Gore and Associates Elkton, MD

• Slashed data access time by 87% for manufacturing operations by consolidating 8 data sources (machine logs, lab data) into a unified analytics database that enabled faster anomaly investigation

• Boosted factory efficiency by 10% by analyzing factory machines, product test data, and customer feedback, pinpointing 3 faulty workflows that caused 15% of defects and 2 unpopular raw materials linked to lab failures Applied Data Projects

E-Commerce Data Mart February 2026 – May 2026

• Built a production-style dbt data mart on BigQuery with a 3-layer architecture (staging intermediate marts) and 25+ automated data quality tests covering uniqueness, referential integrity, and surrogate key constraints

• Reduced data debugging time by enforcing strict layer separation so pipeline failures are isolated to a single layer

• Enabled customer retention analysis across 6 years of e-commerce data by designing a monthly cohort retention model connected to a Looker Studio dashboard with revenue, segmentation, product, and cohort decay views NFL Fantasy Analytics Platform May 2025 – Present

• Deployed a full-stack fantasy football analytics web app (Flask, SQLite, Railway) featuring a mock draft tool and player profile pages - replacing manual research with data-driven rankings to power a 3rd-place league finish

• Improved projection accuracy by 18% over baseline by ensembling XGBoost and Ridge Regression models with age curves and 2-season lag features across QB, RB, WR, and TE groups

• Built an automated ETL pipeline ingesting NFL play-by-play data of 500+ players that engineered position specific features and refreshed rankings, projections, and team grades every game week

• Integrated the Sleeper API to sync live fantasy league rosters, draft picks, and draft board state in real time Technical Skills

Programming: Python (Pandas, NumPy, Scikit-Learn, FastAPI, Flask), Javascript (React), TypeScript Analytics & BI: Looker Studio, Tableau, Power BI, BigQuery, SQL, Streamlit, Microsoft Fabric ML & AI: XGBoost, Linear/Ridge Regression, LangGraph, RAG pipelines, Claude Code, Codex Data Transformation & Pipelines: GCP Cloud Functions, dbt Core, Vertex AI, Snowflake Cortex Cloud & DevOps: Google Cloud Platform, Microsoft Azure, Docker, Railway, Git/Github

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