I am a passionate and driven Data Engineer with a strong foundation in Computer Science and Engineering. I completed my Bachelor of Technology (B.Tech) in Computer Science and Engineering from Kalinga Institute of Industrial Technology (KIIT) with a CGPA of 9.49. My academic background has equipped me with a solid understanding of data structures, algorithms, programming languages, and cutting-edge technologies that form the backbone of modern data engineering.

Currently, I am working as an Analyst at Tredence Analytics Solutions, where I contribute to high-impact data engineering projects, particularly the Unified Data Model (UDM) initiative for Mars Inc. In this role, I work on designing Source-to-Target mappings, developing and maintaining data pipelines using Azure Data Factory, and creating SQL tables in Databricks using Python and PySpark. I am also involved in performance optimization and debugging, ensuring that the data models are reliable and efficient.

My previous internship at Celebal Technologies allowed me to deepen my skills in data science, where I worked on predictive modeling and data analysis projects. I successfully built customer churn prediction models and regression-based air quality index forecasting, achieving high accuracy and low error rates. These experiences have given me a strong understanding of machine learning, statistical modeling, and data preprocessing.

I have developed expertise in various programming languages, including Python, Java, C/C++, and SQL. I am proficient in using tools and frameworks such as Apache Spark, TensorFlow, Keras, Scikit-learn, XGBoost, and Apache Kafka, and I have experience working with cloud platforms like Amazon Web Services (AWS) and Google Cloud Platform (GCP). In addition, I am comfortable working with libraries like Pandas, NumPy, OpenCV, and Matplotlib, which enable me to build robust data solutions and machine learning models.

My passion for data engineering and analytics drives me to continuously explore new technologies and methodologies to solve complex problems. Whether it's designing scalable data architectures, developing real-time data pipelines, or implementing machine learning models, I am always eager to learn and innovate.

Through my academic projects, work experience, and continuous learning, I have gained a comprehensive understanding of data engineering, analytics, and machine learning, and I am excited to apply my skills to create impactful solutions. I am also committed to keeping up with the latest trends in data engineering, big data, and cloud computing.

Feel free to explore my work, and I would be happy to connect and collaborate on data-driven projects or innovative initiatives.