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Data Engineer with 3+ years in Cloud Data Engineering & Analytics
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data professional with 3+ years of experience, including 2.4 years as a Sports Data Analyst and current experience as a Data Engineer. Skilled in SQL, Python, Power BI, and Excel for data analysis, reporting, and business insights. Experienced in sports analytics, dashboard development, data validation, and cloud-based data engineering solutions using Azure, AWS, and PySpark.
Great Lakes Institute of Management, Chennai
PGP · Data Science and Data Engineering
August 1, 2022 – June 30, 2023
Savitribai Phule Pune University, Pune
Bachelor of Science · Statistics
August 1, 2019 – June 30, 2022
Veritas Technologies
Data Engineer
September 1, 2025 – Present
Pune, Maharashtra, India
Privan Sports Analyzer Pvt. Ltd.
Sports Data Analyst
April 1, 2023 – September 1, 2025
Nashik, Maharashtra, India
Basketball ShotTracker Analytics Initiative
April 1, 2023 – September 1, 2025
• Supported analytics datasets using Azure Databricks and Azure Data Lake. • Assisted in transforming sports datasets using PySpark. • Worked with near real-time event-driven data for analytics and reporting. • Participated in Azure and AWS-based analytics initiatives.
Cultural Fit Analysis
The candidate has experience in both a sports analytics company and a technology company, demonstrating adaptability to different industry contexts. The project diversity, from sports analytics to cargo shipping, indicates a broad interest and ability to apply data engineering skills across various domains. The listed education in Data Science and Data Engineering aligns well with a data-centric role, suggesting a foundational understanding of the field. The candidate's experience aligns well with the target role of Data Engineer.
Soft Skills & Operational Fit
The candidate's resume indicates experience working closely with coaches and analysts, suggesting good collaboration and communication skills in a professional setting. The ability to troubleshoot production pipelines implies problem-solving and operational awareness. The transition from a Sports Data Analyst to a Data Engineer shows adaptability and a clear career progression towards data engineering.