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Abhimanyu Wadhwa

Data Engineer

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About Me

A skilled data engineer with experience designing and implementing large-scale data pipelines for diverse industries. Proficient in SQL, Python, and cloud technologies such as GCP. Strong problem-solving and communication skills with a focus on delivering high-quality solutions that meet business requirements.

Experience

Capgemini Technology Services Limited

Software Engineer

  • Developed scalable, performant software for a Financial Services client using Java Enterprise Edition (JEE).
  • Fabricated agile delivery methods to deliver iterative code deployments along with liaising with test teams using JIRA.
  • Adopted tools such as Postman while working with REST APIs.
  • Leveraged the power of SpringBoot to create Java based enterprise grade applications.
  • Proactively contributed to the CI/CD pipeline using Jenkins.
  • Expanded user interaction components for the front-end using React JS.
  • Optimised data processing and storage solutions for performance and cost-effectiveness.
  • Actuated big data technologies such as Hadoop and Spark to analyse large datasets and provide insights to the business.
  • Devised and maintained ETL processes to ensure data was properly transformed, cleansed, and loaded into the data warehouse.

Education

Queen Mary University of London

2022 - 2023

Master of Science in Big Data Science

Currently pursuing a Master's degree in the field of Big Data Science. Modules undertaken include - Data Mining, Machine Learning, Applied Statistics, Big Data Processing, Neural Networks and Deep Learning, Distributed Systems and Cloud Computing. Distinction expected overall.

SRM University, India

2017 - 2021

Bachelor of Science in Computer Science and Engineering

Achieved first class with distinction.

Projects

Big Data Processing Using Spark - Ethereum Analysis

A project on Ethereum Analysis using PySpark, as a part of curriculum at Queen Mary University of London. Analysis of Ethereum Transactions and Smart Contracts.

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MLEnd-London-Sounds Project

A Machine Learning project to make predictions based on recorded audio dataset. Please read on GitHub for further information.

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CIFAR-10 Image Classification using a Custom CNN

A solution to the CIFAR-10 image classification problem in PyTorch, implementing a custom architecture following the mentioned specifications. Final validation accuracy of about 86%.

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Skills

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