Ashok Kumar

Led the successful migration of a 50-petabyte on-premises data infrastructure to Snowflake, a modern cloud data warehouse. This project modernized the organization's data ecosystem, enhancing scalability, performance, and reporting capabilities. The migration process involved designing and implementing robust data pipelines, optimizing data quality, and ensuring seamless and secure data transfer with minimal disruption to business operations. Collaborated with stakeholders to define project objectives and mentored junior team members, fostering a culture of collaboration and knowledge sharing.

Technologies Used:

• Snowflake: Cloud data warehouse for scalable and efficient data storage and querying.
• Python: For developing data pipelines and automation scripts.
• Apache Spark: For processing and transforming large-scale datasets.
• On-premises systems: Legacy systems analyzed and migrated from.
• Industry best practices: Applied for data integrity, security, and scalability during the migration process.

Summary:
Spearheaded the implementation of accounting standards (CECL, CCAR, IFRS) in the banking domain to forecast loan risk and automate compliance processes. Leveraged big data analytics, statistical modeling, and machine learning to develop risk forecasting models, ensuring compliance with regulatory frameworks. The project enhanced proactive risk management by utilizing advanced technologies for efficient data analysis, seamless deployment, and scalable application orchestration.

Technologies Used:

• Big Data Tools: Hive, Hadoop, Kafka for processing and analyzing large datasets.
• Programming Libraries: Spark, Pandas for data analysis and pipeline development.
• Machine Learning: Statistical and machine learning algorithms for risk modeling.
• Containerization: Docker for packaging and deploying risk forecasting models.
• Orchestration: Kubernetes (hosted on Google Cloud Platform) for managing containerized applications.
• Cloud Platform: Google Cloud Platform (GCP) for hosting scalable solutions.
• CI/CD Pipelines: Enabled rapid integration and deployment with DevOps tools.

As a Big Data Engineer, designed and developed frameworks and data pipelines to ensure efficient resource allocation, high availability, and seamless data flow through various components. Focused on building scalable, high-performance systems for data processing, quality assurance, and pipeline automation. Played a key role in creating smart clusters to optimize resource utilization and ensure system reliability.

Technologies Used:

• Programming Languages: Python, Java for framework and pipeline development.
• Big Data Tools: HDFS, Spark, Hive, Kafka, Flume for data storage, processing, and streaming.
• Containerization and Orchestration: Docker, DC/OS, Marathon, Mesos for creating smart clusters and managing resources.
• Quality Assurance: Ensured end-to-end data pipeline integrity and quality.

Worked as a Senior Project Engineer at Wipro Technologies, specializing in software development, automation, and big data solutions. Expertise in Python, Shell Scripting, Selenium WebDriver for automation, and MySQL for database management. Skilled in version control using Git and experienced in big data tools like HDFS and Hive. Proficient in developing frameworks using Java and Python.

Technologies Used:

• Programming Languages: Python, Java, Shell Scripting for automation and framework development.
• Automation Tools: Selenium WebDriver for testing and automation.
• Big Data Tools: HDFS, Hive for data storage and processing.
• Database Management: MySQL for data handling and querying.
• Version Control: Git for source code management.