Charan Sai Ravilla

• Experienced Data Engineer proficient in data transformation and building scalable data streaming platforms and data warehouses to support product data foundations, ensuring data accuracy and consistency. • Hands-on experience with Databricks and Apache Spark, implementing and optimizing ETL pipelines and complex data transformations to deliver reliable, high-performance data solutions for development teams. • Strong coding skills in Python and knowledge of Python Spark libraries (e.g., PySpark) for data querying and transformations, contributing to performance tuning and improving data workflows. • Proven ability to design and implement complex data transformations using Apache Spark and distributed data processing, ensuring data security across systems and platforms. • Solid knowledge of data warehousing and data streaming platforms, partnering with cross-functional teams to define data requirements and deliver business insights with strong problem-solving skills. • Adept at leveraging Python for automating data workflows and implementing custom data quality checks, integrating data solutions to ensure accuracy and consistency across systems. • Skilled in Spark SQL and PySpark to build scalable, fault-tolerant data pipelines handling large-scale datasets daily with real-time processing requirements, ensuring data accuracy. • Experienced in managing and enhancing cloud-native data warehouses, implementing clustering, partitioning, and columnar storage for query performance optimization and data transformation. • Strong command over building, scheduling, and monitoring enterprise-level ETL pipelines that move large scale data across multiple environments, ensuring data accuracy and consistency. • Developed secure and auditable data solutions by integrating access policies and RBAC to manage sensitive configuration and secrets for pipelines and services, ensuring data security. • Designed and implemented data validation frameworks using PySpark and SQL to ensure data accuracy, completeness, and compliance with enterprise governance and quality standards. • Collaborated closely with development teams and cross-functional teams in agile environments to gather data requirements, define transformation logic, and deliver business insights. • Demonstrated ability to scale data platforms by optimizing distributed compute resources on Databricks, reducing job execution time and enhancing overall performance, ensuring data accuracy. • Automated data pipeline orchestration using triggers, ensuring timely delivery of data to reporting layers and minimizing pipeline downtime, with strong problem-solving skills. • Implemented complex joins, window functions, and advanced aggregations in SQL and PySpark for comprehensive data transformation and