Ravi Kant Sharma

Data Analyst

Delhi, India 2+ yrs exp 85 · Excellent

About

Data professional with 2 years of experience leveraging SQL, Power BI, and Python (Pandas) to analyze product and operational data. Experienced in product analytics, KPI tracking, dashboard automation, and data quality validation to support data-driven decision-making. Proven ability to translate complex datasets into actionable insights that drive product and business decisions.

Skills & Expertise (20)

Power BI Intermediate

7.6/10

Years Exp

SQL Intermediate

7.4/10

Years Exp

Retention Analysis Intermediate

7.4/10

Years Exp

Root Cause Analysis Intermediate

7.4/10

Years Exp

Cohort Analysis Intermediate

7.4/10

Years Exp

Python Intermediate

7.0/10

Years Exp

Pandas Intermediate

7.0/10

Years Exp

Microsoft Excel Intermediate

6.6/10

Years Exp

Cross-Functional Collaboration Intermediate

6.0/10

Years Exp

Stakeholder Management Intermediate

6.0/10

Years Exp

Hypothesis Testing Intermediate

6.0/10

Years Exp

AB Testing Intermediate

6.0/10

Years Exp

Statistics Intermediate

6.0/10

Years Exp

Funnel Analysis Intermediate

6.0/10

Years Exp

Jupyter Intermediate

6.0/10

Years Exp

MySql Intermediate

6.0/10

Years Exp

Postgresql Intermediate

5.6/10

Years Exp

Tableau Intermediate

5.4/10

Years Exp

MS SQL Server Intermediate

5.2/10

Years Exp

EDA

Work Experience

Data Analyst

Hive

Apr 2024 - Present

Designed and tracked retention KPIs for Chatous (in-house app), using SQL cohort analysis on 50K+ users to identify two critical drop-off points. Partnered with the product team to implement improvements, increasing user retention by 7%. Built Power BI dashboards providing 15+ product and operations stakeholders with real-time KPI visibility, replacing three manual Excel workflows and reducing ad hoc reporting by 8 hours per week. Performed root cause analysis across 3+ dashboards, identifying a broken SQL join that caused reporting discrepancies. Resolved upstream logic issues and restored reporting accuracy for 20+ daily stakeholders. Reduced data error complaints by 30% by identifying root causes across SQL reporting pipelines and implementing automated validation checks that detected issues before reports were published.