Meghansh M

Data Analyst

Talent

JaipurMember since August 7, 2026

SQLJoinsAggregationsPythonEDAPower BIETL

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SQLJoinsAggregationsPythonEDA

About

Detail-oriented B.Tech (IT) student and Data Analyst with hands-on experience diagnosing and resolving data-quality issues across multi-table, API-sourced datasets. Built a fraud/bot-detection flagging system that identified the root causes of illegitimate account activity and protected campaign budgets direct experience with troubleshooting, issue documentation, and analytical problem solving. Comfortable reading and reasoning about SQL and Python code, and translating technical findings into clear, stakeholder ready explanations for non-technical audiences via Power BI dashboards.

Currently building a live REST API-based ETL and analytics pipeline as part of a fintech capstone project.

Experience

Data Analytics Intern at Trainity (2025-07 – Present)

Diagnosed structural issues in raw multi-table data and resolved them by designing relational schemas and table-level key relationships, producing an analysis-ready model.
Investigated user behavior trends via SQL joins/aggregations and Python-based EDA, documenting descriptive statistics for stakeholder review.
Built interactive Power BI dashboards that converted raw data into stakeholder-ready KPIs, enabling faster, data-driven decisions across teams.

Mutual Fund Analytics — Capstone Project at Bluestock Fintech (2026-06 – Present)

Building an end-to-end analytics pipeline analyzing top Indian mutual funds' bull-run performance using live data ingested via REST APIs, spanning ETL, EDA, fund-metrics calculation, and dashboard deployment.

Currently at the EDA stage after completing initial ETL; fund-metrics calculation and dashboard deployment are next.

Instagram User Analytics — Project at Trainity (2025-07)

Wrote filtered, aggregated SQL queries to segment users by activity level and loyalty signals, directly informing a rewards and advertisement campaign.
Engineered a unique-ID-based flagging system to detect, diagnose, and exclude bot and illegitimate accounts from API-sourced data — a root-cause troubleshooting workflow that safeguarded campaign spend.