Data Analyst with 2+ years of experience building dashboards and data pipelines that business teams actually use. I work across the full analytics cycle — raw extraction, SQL transformation, Power BI reporting, and stakeholder delivery.
Currently pursuing my MBA in Finance at the University of Delhi, bridging the gap between data and business strategy.
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│ 📍 Location Delhi, India · Remote-ready · Hybrid-open │
│ 💼 Seeking Data Analyst roles │
│ 🎓 Education MBA Finance · BCom (Accountancy) · University of Delhi │
│ 📊 Impact 40% reduction in manual reporting · 5+ data sources │
│ ⚽ Football CRPF Camp Team · Subroto Cup Runners-Up │
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Languages & Analytics
Data Visualization & BI
Databases
Data Engineering & Tools
🏢 Medianama · Data Analyst · Jun 2024 – Feb 2025
Joined as the sole analyst — owned the full reporting stack from raw data to executive dashboards.
| Metric | Result |
|---|---|
| Manual reporting time | ↓ 40% via Power BI automation |
| Data sources integrated | 5+ into a single PostgreSQL star schema |
| Stakeholder dashboards | Real-time Power BI with live refresh |
- Extracted and cleaned data from 5+ disparate sources, built a normalized star schema in PostgreSQL, and connected it directly to Power BI for consistent, automated reporting
- Created Excel performance dashboards used by the editorial team to produce data-driven business articles
- Reduced end-to-end reporting cycle from manual to automated, freeing analyst time for deeper analysis
🏢 Bajaj Allianz General Insurance · Data Analyst Intern · May 2023 – Jan 2024
Built reporting infrastructure used by senior financial leadership across 15+ insurance products.
| Metric | Result |
|---|---|
| Manual reporting time | ↓ 25% via Power BI automation |
| Insurance products analyzed | 15+ (SWT, ULIP, E-Touch II, AWG Platinum…) |
| Pipeline cadence | Monthly PostgreSQL pipelines, fact-dimension models |
- Automated Power BI dashboard for senior financial leadership enabling detailed analysis of customer insurance policies
- Built fact-dimension data models in PostgreSQL to support scalable, repeatable BI reporting
- Analyzed cross-product customer data to surface win-back opportunities that improved conversion and revenue
| # | Project | What It Does | Stack | Status |
|---|---|---|---|---|
| 01 | E-Commerce Sales Analytics | End-to-end pipeline: raw CSVs → PostgreSQL → Python clean → DuckDB OLAP → Seaborn visuals | PostgreSQL · Python · DuckDB · SQLAlchemy | ✅ Shipped |
| 02 | Retail Data Warehouse | Star schema dimensional model with fact/dim separation, SCD handling, BI-ready output | PostgreSQL · SQL · Power BI | ✅ Shipped |
| 03 | Databricks Medallion Warehouse | Production Airflow + dbt pipeline across Bronze → Silver → Gold layers | Databricks · Airflow · dbt | ✅ Shipped |
| 04 | Bike Store Relational Database | Unstructured data → 9-table normalized schema with stored procs, triggers & views | PostgreSQL · SQL | ✅ Shipped |
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║ $ echo "Open to Data Analyst roles. Let's talk data." ║
║ > Open to Data Analyst roles. Let's talk data. ║
║ $ _ ║
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