Venkata Kosuri

• Data Engineer with 5+ years of experience designing scalable data solutions and data warehouses, proficient in data transformation and ensuring data accuracy across systems. Collaborating with development teams to deliver reliable, high-performance data solutions. • Specialized in developing ETL pipelines using Python and PySpark for processing structured datasets, with solid knowledge of Apache Spark and distributed data processing. Proven ability to implement complex data transformations. • Hands-on experience with Databricks and data streaming platforms, contributing to performance tuning and improving data workflows. Strong problem-solving, analytical, and communication skills. • Built data ingestion frameworks to process large-scale datasets, enabling timely data analysis. Partnering with cross-functional teams to define data requirements and deliver business insights. • Designed and implemented solutions to ensure data security and consistency across systems, meeting governance requirements. Strong experience coding in Python for data engineering tasks. • Created parameterized and reusable pipelines to improve ETL maintainability and accelerate delivery of analytics-ready datasets. Familiarity with Snowflake, a plus for data warehousing projects. • Engineered near real-time streaming pipelines, improving operational visibility and enabling proactive alerting. Focused on optimizing ETL pipelines and complex data transformations. • Configured observability pipelines to track job metrics, monitor performance, and trigger alerts for SLA breaches. Ensuring data accuracy, consistency, and security across systems. • Migrated on-premise Hadoop and traditional ETL jobs to cloud-native environments, improving performance and scalability. Hands-on experience with Databricks for data processing. • Implemented data profiling and validation logic to automate data quality checks and schema compliance across enterprise pipelines. Strong knowledge of Apache Spark and distributed data processing. • Developed end-to-end solutions using Azure Data Factory, Databricks, and Synapse to support ingestion, transformation, and delivery of analytical datasets. Experience with data warehousing. • Integrated real-time data sources using Apache NiFi, Kafka, and shell scripting to orchestrate hybrid ingestion flows. Proven ability to design and implement complex data transformations. • Designed high-performance dimensional data models using star and snowflake schema patterns to optimize analytical queries. Solid knowledge of Apache Spark and distributed data processing. • Automated CI/CD workflows using Git, Azure DevOps, and Terraform for