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Data Engineer with less than a year in big data processing & analytics.
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Highly motivated Data Analyst and Data Engineering Intern with 8 months of hands-on experience in developing and optimizing big data processing workflows using Apache Spark, Databricks, and Microsoft Azure. Proficient in designing end-to-end data pipelines, implementing data ingestion and transformation, and creating Power BI dashboards for KPI reporting. Adept at data cleaning, analysis, and modeling to derive actionable business insights and improve data processing efficiency.
JECRC University
Bachelor of Technology · Computer Science and Engineering
January 1, 2021 – July 1, 2025
GKM IT
Data Engineering Intern
September 1, 2025 – November 1, 2025
Udaipur, Rajasthan, India
KVON Tech Pvt. Ltd.
Data Analyst Intern
March 1, 2025 – July 1, 2025
Jaipur, Rajasthan, India
Big Data Processing with Apache Spark
June 1, 2026 – Present
Implemented a distributed data processing pipeline using Apache Spark (DataFrames & Spark SQL) to efficiently analyze large-scale datasets. Performed data exploration, cleaning, transformation, and integration across multiple datasets to create a unified dataset for scalable analytics. Achieved 70% reduction in processing time compared to traditional processing methods by leveraging Spark's distributed computing capabilities.
End-to-End Data Engineering Pipeline on Azure
June 1, 2026 – Present
Built an end-to-end data engineering pipeline on Microsoft Azure, integrating SQL and NoSQL databases for scalable data storage and management. Implemented data ingestion and transformation using Azure Data Factory and Azure Databricks, enabling efficient processing of large datasets. Designed data models and views for analysis and reporting, improving data accessibility and achieving 50% faster insight generation.
Big Data Analytics using Databricks
June 1, 2026 – Present
Utilized Databricks (Apache Spark) to process and analyze large-scale datasets, leveraging the platform's distributed computing capabilities. Developed Databricks notebooks to implement Spark-based data transformations and analytics, enabling interactive data exploration and processing. Automated SQL-based workflows within Databricks, improving data processing efficiency and achieving 40% faster analytics compared to conventional methods.
Google Cloud Career Readiness - Google Cloud Skills Boost Hybrid
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects and internships demonstrate a strong alignment with the target role of Data Engineer, showcasing relevant skills and technologies. The diversity of projects, covering Azure, Spark, and Databricks, indicates a broad interest in modern data engineering practices. The candidate is still pursuing a bachelor's degree, which suggests a learning-oriented mindset. However, all experience is academic or internship-based, which might require mentorship in a professional setting.
Soft Skills & Operational Fit
The candidate's project descriptions and internship experiences highlight an ability to work on complex technical tasks, optimize processes, and achieve measurable results. The focus on efficiency improvements (e.g., 70% reduction in processing time, 40-70% faster data processing) suggests a results-oriented approach. While direct evidence of collaboration or stress handling is not explicitly provided, the successful completion of academic projects and internships implies a degree of operational fit.