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Mayuri
Data Analyst
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Competencies
Demonstrated experience with Python.
Demonstrated experience with SQL.
Demonstrated experience with MongoDB.
Demonstrated experience with Data Analysis.
Demonstrated experience with Data Cleaning & Transformation.
Education
Summary
Data Analyst open to new opportunities
Experience
Data Analyst
Use SQL to extract, clean, validate, and analyze business and operational datasets.. Use Python for data preparation, analysis, automation, and validation of recurring datasets and reports.. Develop SQL queries to identify missing, duplicate, inconsistent, and incomplete data.. Automate recurring data-processing and analysis tasks using Python and SQL to reduce manual effort.. Perform data cleaning and transformation using Python and SQL to prepare datasets for analysis.. Build reusable Python scripts and SQL queries for data validation, reconciliation, and quality checks.. Develop analytical reports and dashboards using Power BI to communicate trends, KPIs, and business insights.. Analyze business data to identify trends, patterns, and process issues and provide actionable insights.. Translate business requirements into structured SQL queries, Python-based analysis, and stakeholder-ready reporting.
Data Analyst
Developed and optimized SQL queries to extract, transform, validate, and analyze large financial datasets.. Used Python to perform before-and-after analysis and evaluate changes in reporting processes, data quality, and error rates.. Developed data-validation logic using SQL and Python, including row-count, cross-total, null, and duplicate checks across 15+ recurring deliverables.. Automated recurring data-processing and analysis tasks using Python and SQL to reduce manual reporting effort.. Analyzed large datasets to identify trends, inconsistencies, and data-quality issues.. Developed Power BI dashboards and automated reporting solutions to present analytical findings and business KPIs.. Translated business requirements into structured SQL queries and Python-based analytical solutions.. Provided analytical findings to support data-driven business decisions.
Data Analyst Intern
Used Python to analyze client-engagement data across 40–50 accounts and identify patterns and risk indicators.. Performed data preparation, exploratory analysis, and validation using Python.. Applied statistical analysis to compare predicted outcomes with actual results.. Used SQL to retrieve, filter, aggregate, and analyze business datasets.. Developed Power BI and Excel-based analytical reports to communicate findings to non-technical stakeholders.. Translated business requirements into structured analytical outputs using Python and SQL.. Delivered data-driven findings and recommendations to cross-functional stakeholders.. Supported recurring analytical requests and maintained on-schedule delivery across 15+ reports.
Data Analyst Co-op
Used advanced SQL, including CTEs and window functions, to clean and analyze 50,000+ records.. Developed complex SQL queries for data extraction, filtering, aggregation, and analysis.. Used Python for data preparation, exploratory analysis, and validation of analytical datasets.. Worked with MongoDB to analyze and structure unstructured data sources for analytical use.. Performed data-quality checks to identify missing, duplicate, inconsistent, and anomalous records.. Translated ad hoc business questions into structured SQL queries and Python-based analyses.. Presented analytical findings and actionable insights to support stakeholder decision-making.
Supply Chain Analyst Intern
Used Python and SQL-based analysis to evaluate operational and supply-chain datasets and identify trends.. Performed data cleaning, transformation, validation, and exploratory analysis on inventory and merchandising data.. Applied statistical analysis, hypothesis-driven approaches across 100+ retail locations.. Developed data-validation checks to identify inconsistencies and anomalies in operational datasets.. Used Power BI and Tableau to develop and improve dashboards for operational and business stakeholders.. Analyzed inventory and sales data to identify trends and support replenishment decisions.. Translated business questions into structured data analyses and actionable recommendations.