Highly motivated Data Engineering and Analytics professional with a B.Tech in Electronics and Communication, specializing in cloud-based ETL, big data processing, and advanced analytics. Proven ability to design and optimize scalable data pipelines on GCP and Hadoop, create interactive dashboards, and deliver actionable insights. Eager to leverage strong technical skills in PySpark, BigQuery, and Looker Studio to drive data-driven strategies and enhance business outcomes.
Data Engineering and Analytics Intern
→
Summary
Spearheaded the development and optimization of cloud-based ETL pipelines and real-time processing solutions to deliver actionable business insights within an agile data engineering environment.
Highlights
Built and optimized ETL pipelines using PySpark, Apache Spark, and Hive to efficiently process large datasets, significantly improving data processing speed and reliability.
Designed and implemented scalable, cost-effective data pipelines on Google Cloud Platform (GCP) leveraging BigQuery, Dataproc, Cloud Storage, and Cloud Composer.
Developed real-time and batch processing solutions within the Hadoop ecosystem, integrating structured and semi-structured data sources for unified analytics.
Created interactive dashboards and reports using Looker Studio to visualize key performance indicators (KPIs) and deliver actionable business insights to stakeholders.
Collaborated effectively in an agile environment, building end-to-end data engineering solutions while adhering to best practices for data quality, transformation, and performance optimization.
Python (Pandas, NumPy, PySpark), SQL (MySQL, PostgreSQL), Scala.
MySQL, PostgreSQL, MongoDB, BigQuery, Snowflake.
Apache Spark, Apache Airflow, Kafka, Hive, Dataproc, Cloud Composer, dbt.
GCP (BigQuery, Cloud Storage, Dataproc, Dataflow), AWS (S3, Glue, Redshift).
Tableau, Power BI, Looker, Excel, Matplotlib, Seaborn.
Star/Snowflake Schema, OLAP/OLTP concepts, Fact & Dimension tables.
Git, Docker (basics), CI/CD (Jenkins or GitHub Actions).
→
B.Tech
Electronics and Communication
Grade: 6.91/10 CGPA
Courses
Database Systems
Data Warehousing
Big Data Processing
Issued By
MICROSOFT
Issued By
Issued By
Issued By
CHEGG
Issued By
ACCENTURE
Issued By
YBI Foundation
Summary
Implemented automated pipelines to ingest and process high-volume banking transactions using distributed compute frameworks on cloud clusters, focusing on data integrity, anomaly detection, and regulatory reporting.
Summary
Designed and deployed a cloud-based ETL pipeline to integrate multi-format sales data from eight countries into a centralized data warehouse, incorporating predictive models for enhanced decision support.