Data Analyst Big

DIVYANSH PRAKHAR SONI

***********@*****.*** LinkedIn Tableau +1-862-***-**** Chicago, IL

SUMMARY

Data Analyst with 4 + years of experience at the intersection of big data engineering and analytics. I design scalable pipelines, craft interactive BI dashboards, and leverage cloud platforms like Databricks, Snowflake, and AWS to turn massive datasets into clear, actionable insights that drive business outcomes.

PROFESSIONAL EXPERIENCE

JPMorgan Chase & Co. Feb 2025 – Present

Data Analyst IL, USA

● Built scalable big data batch pipelines on Azure Databricks with Spark to process terabytes/day of financial data in ADLS, enabling efficient transformation and querying in Azure Synapse for real-time trading and risk insights.

● Streamlined financial reporting using Power BI, DAX queries, and Agile methods, saving 2 hours each week and helping a nonprofit consistently deliver key reports on time.

● Led financial analysis for 35+ client portfolios using Python, delivering ROI and cost insights to support leadership in data- driven investment decisions.

● Developed Power BI solution integrated with centralized Snowflake database to manage, visualize 500+ financial records, ensuring accurate querying, consistent insights for portfolio performance tracking. Illinois Institute of Technology May 2024 – Dec 2024 Data Analyst IL, USA

● Built interactive Tableau dashboards to visualize key placement KPIs, helping all the IIT colleges streamline decisions and reduce analysis time from 10 to 6 hours per week.

● Fully automated SEVIS data processing employing Advanced Excel (conditional formulas, index matching, VLOOKUP), VBA, identifying incoming students with valid visas, cutting manual effort by 60%.

● Designed and implemented ETL workflows for 5,000+ student records using Python, Apache Airflow and SQL, improving data accuracy and automating enrolment verification, cutting staff effort by 75%. Accenture Jan 2020 – Dec 2022

Data Analyst India

● Drove targeted marketing campaigns by analyzing sales data Tableau (using LODs, parameters and calculated fields), uncovering underperforming products and regions, and increasing partner revenue by 15% in two quarters.

● Increased Lenovo’s partner program ROI by 25% through strategic budget reallocation, guided by performance modelling and data analysis using Python and SQL on Amazon Redshift.

● Conducted A/B testing on offer banners in the Lenovo Partner Portal, boosting redemptions by 10% and partner engagement; demonstrated results to clients across 100+ countries to inform future upsell strategies.

● Built scalable ETL pipelines using AWS Glue and S3 to automate processing of partner activity and offer redemption data, enabling real-time insights for global campaign performance tracking.

● Gathered business requirements from stakeholders translating them into functional specifications using Agile methodologies, breaking down tasks into user stories ensuring iterative development, continuous feedback. EDUCATION

Illinois Institute of Technology, Chicago, IL (Master of Science in Computer Science) Jan 2023 – Dec 2024 Abdul Kalam Technical University, India (Bachelor of Technology in Information Technology) Aug 2016 – Sep 2020 PROJECTS (More available on GitHub )

IPL Cricket Data Analysis

● Analyzed IPL datasets using Databricks, PySpark, Python, AWS EC2, and Jupyter Notebook, with interactive visualizations to uncover trends in team performance, player stats, and match outcomes at scale. SKILLS

● Programming & Databases: Python, SQL, R, MySQL, PostgreSQL, Oracle, MongoDB, NoSQL, Snowflake

● Tools: Power BI, Tableau, Looker, Microsoft Excel, Git, GitHub, supply chain, Visual Studio, Jira

● ETL & Data Integration: Informatica, Kafka, Apache HTTP, Automation, Apache Airflow, Databricks, AWS Glue

● Libraries, Methodologies: NumPy, Pandas, dplyr, SciPy, PySpark, Matplotlib, Openpyxl, Seaborn, Waterfall, Agile

● Data Analysis: Data Mining, Data Governance, A/B Testing, Data Warehousing, Regression, ETL pipelines

● Cloud Platforms: AWS (Athena, Glue, Sagemaker, Lambda, S3, RDS, Redshift, DynamoDB, Quicksight), Azure

● Big Data & Machine Learning: Hadoop, Spark, Data Modeling, Clustering, Classification, LLMs, Gradient Descent

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