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Data Analyst Power Bi

Location:
San Diego, CA
Posted:
December 05, 2024

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Resume:

PRATHAMESH JALGAONKAR

San Diego, CA 929-***-**** *************@*****.*** linkedin.com/in/prathameshjalgaonkar Portfolio EDUCATION

San Diego State University Aug 2022 – May 2024

Master of Science in Information Systems San Diego, CA Coursework: Enterprise Database Management, Statistical Analysis, Business Analytics, Time Series Analysis, Object-Oriented Programming (Advanced Python), Big Data Infrastructure, Cloud Computing Pune University Aug 2018 – June 2022

Bachelor of Engineering in Electronics & Telecommunication Pune, India SKILLS

Analysis & Visualization Tools: Power BI, Tableau, Excel, PowerApps, Power Automate, SharePoint, Alteryx, Hadoop Database Management Systems: Microsoft SQL Server, MySQL, Oracle, PostgreSQL, NoSQL, MongoDB, AWS, Microsoft Azure Programming Technologies: Python (Pandas, NumPy, Matplotlib), R, SQL, APIs EXPERIENCE

San Diego State University’s Digital Innovation Lab Sep 2024 – Present Data Analyst San Diego, CA

• Developed automated scripts for extracting dynamic web data using Python and Selenium, streamlining data collection and ensuring high accuracy in data availability for analysis.

• Built interactive components to simulate user actions (scrolling, clicking, expanding content), capturing hidden and dynamically loaded data for comprehensive datasets.

• Documented processes & provided quality assurance checks to maintain data integrity throughout collection & transformation. A2Z POS Jan 2024 – July 2024

Data Analyst Intern San Diego, CA

• Utilized AWS Cost Explorer API to analyze and visualize service costs, driving cost optimization and budget forecasting.

• Created a stakeholder-level data collection app using PowerApps, linking task to projects and managing employment resources.

• Developed a Power BI dashboard to present key metrics, enhancing data-driven decision-making for stakeholders.

• Developed data pipelines using SharePoint for seamless project and task data synchronization.

• Partnered with stakeholders to define requirements, customize PowerApps, and build automated flows for project planning, continuously refining data workflows and visualizations. Wizeal July 2021 – July 2022

Data Analyst Mumbai, India

• Developed & maintained ETL pipelines to integrate data from security equipment logs, scheduling systems, & power equipment usage metrics, using Python, SQL, & API integrations, ensuring accurate & up-to-date datasets for analysis.

• Analyzed equipment usage patterns and manpower deployment efficiency, identifying cost-saving opportunities and operational bottlenecks, which led to a 15% improvement in resource allocation.

• Designed and maintained data dashboards in Power BI to track KPIs, providing real-time visibility into service performance and client satisfaction rates.

• Assisted in maintaining, optimizing, and querying databases in MySQL and Oracle to support data-driven decision-making across various business units.

PROJECTS

Olympics Data Analytics Azure, SQL, Power BI July 2024

• Built an end-to-end data pipeline using Azure Data Factory, Databricks (Apache Spark), and Synapse Analytics to process data on 11,000 athletes, 47 disciplines, and 743 teams from the Tokyo Olympics.

• Derived insights on medal counts and participation rates with SQL-based analytics in Power BI.

• Optimized data pipelines, cutting data latency by 30% and improving decision-making efficiency by 25%. Serverless Web Application on AWS Python, AWS May 2024

• Developed a scalable serverless web app using AWS S3, Lambda, DynamoDB, CloudFront, and Route 53.

• Integrated Lambda and DynamoDB for dynamic page view tracking and deployed a Python app on EC2 for visit monitoring.

• Enhanced security with IAM roles, SSL/TLS encryption, and access control via Route 53. Serverless Web Application on AWS Python, AWS Feb 2024

• Built and populated a PostgreSQL database using Python (pandas, psycopg2) to store and manage multi-year financial data.

• Designed a relational schema with three tables for efficient querying of over 1M rows of financial data.



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