Data engineer with expertise in converting complex business requisites into sophisticated data architectures, predictive models, and comprehensive analytical reporting solutions.•Proficient in the complete lifecycle of machine learning processes encompassing Data Acquisition, Data Preparation, Data Manipulation, Feature Engineering, Application of Machine Learning Algorithms, Model Validation, and Data Visualization.•Experienced in Machine Learning, Mathematical Modeling, and Operations Research, with proficiency in R and Python.•Proficient in Python and its libraries such as NumPy, Pandas, Scikit-Learn, Matplotlib, and Seaborn.•Experienced in working with deep learning frameworks TensorFlow, Keras, and PyTorch, and employing Databricks for efficient management and deployment of these models.•Proficient in utilizing Integrated Development Environments (IDEs) such as Jupyter Notebook, Visual Studio Code, and Sublime Text for software development and data analysis projects.•Experienced in Big Data, HDFS, Map Reduce, and PySpark.•Skilled in constructing Data Warehousing and ETL pipelines utilizing Spark, Airflow, and various cloud-based technologies, with significant experience in leveraging Databricks for scalable big data solutions and machine learning workflows.•In-depth knowledge of Cloud Infrastructure like AWS S3, AWS EC2, AWS EMR, Sagemaker, AWS Lambda, Docker, and extensive use of Databricks for streamlining data processing and analytical tasks.•Successfully operates within a high-pressure, multitasking environment, demonstrating proficiency both independently and as an integral member of a collaborative team.•Expert in deploying and managing big data architectures and applications using Databricks’ unified analytics platform to drive operational efficiency and insight.