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Devarsh patel

@devarshpatel

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Data Analyst turning SQL and Python insights into KPI-driven product decisions.

United States

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What I'm looking for

I’m looking for a role where I can own KPI dashboards, build real-time data pipelines on AWS, and use cohort/funnel and predictive modeling to guide product and marketing decisions. I want to communicate insights clearly to executives and influence strategy.

I’m a results-driven Data Analyst with a Master’s in Computer Science and hands-on experience delivering actionable insights across SQL, Python, Tableau, and Power BI. I build executive dashboards, define KPI frameworks, and translate complex analysis into clear, stakeholder-ready narratives.

In my recent role, I designed and maintained Tableau and Power BI executive dashboards for acquisition, retention, churn, and ROI KPIs. I also architected an AWS cloud data warehouse (Redshift, Glue, Athena, S3) with automated ETL pipelines processing 500K+ daily events—reducing data latency from 24 hours to real-time.

I apply analytics end-to-end: cohort and funnel analysis, A/B testing, and predictive churn modeling using Scikit-learn and Python (Pandas, NumPy). The impact has included improved targeted marketing performance (+22%), churn reduction (12%), and conversion lift (14%), along with weekly CEO-ready data storytelling.

Experience

Work history, roles, and key accomplishments

Current

Data Analyst

Current

Geoflixz+

Dec 2024 - Present (1 year 6 months)

Designed and maintained Tableau and Power BI executive dashboards for a streaming platform (10,000+ active users), improving acquisition, retention, churn, and ROI reporting for C-suite decisions. Architected AWS Redshift/Glue/Athena/S3 ETL pipelines processing 500K+ daily events, reducing data latency from 24 hours to real time; cohort segmentation improved targeted marketing performance by 22% a

Tableau Power BI SQL NumPy AWS RedShift AWS Glue S3 Cohort Analysis predictive modeling

Junior Data Analyst

Kpiteng

Jul 2022 - Jul 2023 (1 year)

Analyzed application usage data using SQL and Python, running funnel analysis and feature adoption tracking that contributed to a 15% increase in user engagement. Built Tableau dashboards and weekly analytics reports, defined KPIs and data quality standards, and reduced application load time by 10% through A/B testing of optimization strategies.