Sandeep Raj Chinnakandukur

Charlotte, North Carolina • ad5bfx@r.postjobfree.com • +1-980-***-**** • LinkedIn

PROFESSIONAL SUMMARY

Experienced Data Analyst with over 3 years of hands-on expertise in statistical analysis, predictive modeling, and large-scale data project

management. Demonstrated expertise in SQL for data transformations and Python for data wrangling and statistical analysis for generating

actionable insights. Proficient in MS Office Applications and Excel, including Pivot Table, Lookups, PowerPivot, Index, and PowerBI for

dashboarding and reporting. Capable of collaborating with stakeholders and presenting quality deliverables to drive business growth.

SKILLS

Functional: PowerBI, Tableau, Git, Microsoft Excel, Agile, JIRA, Jenkins, Bit Bucket, Salesforce, Slack.

Programming Languages: Python, R, Scala, SQL, PL/SQL, NoSQL, Shell scripting, Java, C#, HTML5.

Tools/Frameworks: Microsoft Office, Scikit-Learn, Azure Databricks, Plotly, Pandas, Matplotlib, AWS Quicksight and Amazon Sagemaker.

Databases: MS SQL Server, Azure Synapse Analytics, AWS Redshift, BigQuery, Snowflake, Oracle, Amazon DynamoDB

Soft Skills: Leadership, Mentoring, Team Collaboration.

PROFESSIONAL EXPERIENCE

Infosys - Hyderabad, India Aug'19 - Jul'22

Data Analyst Associate Remote

• Developed Power BI dashboards for a 10-member cross-functional team and utilized Python for Exploratory Data Analysis (EDA),

aiding in identifying and reducing financial discrepancies by 25%, thereby enhancing data reliability for decision-making.

• Enhanced financial reporting efficiency by implementing Excel automation with VLOOKUP, data validation, Power Query, and Pivot

Tables, leading to a 20% faster report generation and improved operational workflows.

• Collaborated with the product marketing team to integrate Google Analytics and marketing campaign data from Snowflake,

focusing on KPIs such as open rate, click-through rate, bounce rate, and revenue per click.

• Implemented an Amazon Aurora Database and Redshift to securely store business insights and operations data increasing storage

capability by 50% while reducing latency time by 250%.

• Developed complex SQL statements using stored procedures and CTEs to extract data from the Snowflake warehouse server,

supporting report building by developing different chart types including Pie Charts, Bar Charts, Tree Maps, Circle Views, Line

Charts, Area Charts, and Scatter Plots in Power BI.

• Implemented ETL data pipelines using Azure Data Factory to migrate the 480,034 rows of data from AWS S3 to Azure Data Lake.

• Integrated AWS Step Functions with CI/CD pipelines, reducing deployment time by 40% and increasing release frequency by 50%.

Infosys - Mysore, India Jan'19 - May'19

Data Analyst – Product Team Onsite

• Conducted in-depth root cause analysis for production issues and successfully addressed over 300 defects. Utilized automated SQL

queries to analyze logs, resulting in a notable enhancement of the team's issue resolution efficiency by approximately 20%.

• Actively coordinated with cross-functional teams in an Agile environment to deliver a strategic data migration project on

schedule, reducing project delivery timelines by 15% through efficient sprint planning.

• Used Tableau to create interactive visualizations, including Scatter Plots and Density Charts of data which tracks 20+ Product KPIs.

EDUCATION

University of North Carolina at Charlotte, Charlotte, NC. Aug'22 - Dec'23

Master of Science in Computer Science GPA: 3.9

Relevant coursework: Intelligent Systems, Visual Analytics, Big data analytics on Cloud, Software Engineering, Automated Software

Engineering, Object Oriented Design, Project Management, Internet Protocols, Digital Security

PROJECTS

Data Analysis of Rock Fracture Patterns from Acoustic Emissions Using AWS Feb'23 - Apr'23

• Conducted extensive big data analysis on AWS, leveraging diverse tools like Amazon S3, AWS Glue, and AWS Sagemaker to explore rock

fracture patterns from acoustic emissions.

• Utilized AWS Quicksight for result visualization and insightful data extraction, contributing significantly to scientific research on the

fracturing energy of rocks across varied weather and temperature conditions.

Loss Ratio Prediction - Auto Insurance Portfolios using Open-source ML Models Oct'21 - Dec'21

• Built a prediction model leveraging classification algorithms including Logistic Regression, Decision Trees, and Random Forest, achieving an

accuracy of 85% on test data, with Random Forest outperforming other models with an accuracy of 87%.

• Conducted extensive feature engineering, including feature scaling and selection, to improve performance and interpretability.

• Implemented visualization techniques using Matplotlib and Seaborn to gain insights and Documented project findings,

methodologies, and results for transparency.

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