Data Analyst with internship experience in healthcare (MAX Healthcare) and govtech (EasyGov). I build analytics solutions from scratch — SQL Server schema design, Python data pipelines, and Power BI dashboards with 15+ DAX measures. Currently targeting Data Analyst roles in IT services and product companies across India.
Data & Analytics
Tools & Environment
End-to-end hospital operations dashboard tracing 30-day readmission drivers across a custom-built, India-calibrated dataset — 9,093 encounters across 6 diagnosis categories at a fictional Delhi-NCR tertiary care hospital (FY 2024-25). Built from scratch after confirming no usable real Indian patient-level dataset exists publicly, and calibrated against ICMR disease-burden data and published Indian out-of-pocket cost studies instead of a generic Kaggle dataset.
Tools: SQL Server · Power BI · DAX · Python
| Finding | Value |
|---|---|
| Overall 30-day readmission rate | 17.5% |
| Highest vs lowest-risk category | CKD at 25.2% vs Maternal & Neonatal at 5.1% |
| Age is the cleanest single driver | 4.4% (under 18) → 32.5% (75+) |
| Frequent-flyer cost concentration | 17.5% of patients → 37.4% of total cost |
🔗 GitHub | 📊 Live Dashboard
IT support operations dashboard built on 8,000 tickets across 8 departments and 20 agents — tracking SLA compliance, agent workload, and resolution patterns over 26 months (Jan 2024 – Feb 2026). Schema designed to mirror how ServiceNow and Jira Service Management operate in practice.
Tools: SQL Server · Power BI · DAX · Python
| Finding | Value |
|---|---|
| Overall SLA breach rate | 23.49% — no improvement across 26 months |
| Highest breach category | Network at 37.5% vs Access at 13.1% |
| Agent overload | 3 agents carry 35% of total ticket volume |
| Peak load window | Monday 11am–1pm across all departments |
🔗 GitHub | 📊 Live Dashboard
Product analytics covering the full user lifecycle — 5,000 users, 6,566 sessions, 14,996 feature interactions across a normalised 4-table SQL Server schema (Jan 2024 – Dec 2024).
Tools: SQL Server · Power BI · DAX · Python · Power Query
| Finding | Value |
|---|---|
| Paying and retained users | Only 25.5% of total users |
| Feature adoption vs conversion | 5-feature users convert at 55.3% vs 46.9% |
| MRR lost to churn monthly | $90,548 vs $92,818 retained |
| Free vs paid engagement | Free users average 2.1 mins more per session |
🔗 GitHub | 📊 Live Dashboard
Workforce analytics across 15,000+ job records covering automation risk, salary outcomes, and reskilling urgency across 9 countries and 8 industries (2020–2026). Built with a full data pipeline — Python for data quality engineering, SQL Server for cleaning and transformation, Power BI for a 4-page interactive dashboard.
Tools: SQL Server · Power BI · DAX · Python · Power Query
| Finding | Value |
|---|---|
| High automation risk jobs | 30.12% of all records |
| Most disrupted sectors | Energy (disruption 23.62) and Finance (23.59) |
| Salary outcome post-reskilling | +$3,567 average — but 41.13% of workers earned less |
| Highest skill gap role | Teacher and Customer Support Rep at index 51 |
🔗 GitHub | 📊 Live Dashboard
"Without data, you're just another person with an opinion." — W. Edwards Deming
Open to Data Analyst opportunities · India · Let's connect