Sravana Sodipilli

Over 8 years of experience designing, building, and optimizing large-scale data architectures across Google Cloud Platform (GCP), Amazon Web Services (AWS). Proven expertise in cloud-native data warehousing and analytics platforms including BigQuery, Databricks, and Dataflow, with strong proficiency in ETL/ELT pipeline development using Python, Snow SQL, Spark, and PySpark. Skilled in architecting and integrating data from diverse sources — structured, semi-structured, and unstructured — into centralized data lakes and enterprise data warehouses, ensuring high availability, scalability, and security. Optimized SQL queries, data models, and schemas in Snowflake, and Hive to enhance analytics, reporting, and BI performance. Managed real-time data ingestion with AWS Kinesis into S3 and Redshift, improving data freshness and latency. Experienced in implementing partitioning, clustering, materialized views, and query optimization techniques to improve performance and reduce costs across cloud platforms. Strong background in real-time streaming and event-driven architectures using technologies like Pub/Sub, SNS, SQS, and Cloud Functions for low-latency processing. Proficient in infrastructure-as-code (Terraform) and CI/CD automation (Cloud Build, GitLab CI/CD), enabling faster, repeatable deployments of data pipelines and infrastructure. Deployed and managed containerized applications using Kubernetes (EKS/GKE/AKS), ensuring high availability, scalability, and self-healing of services. Deep knowledge of data governance frameworks, metadata management, and compliance standards (GDPR, HIPAA, SOC 2), leveraging tools like Cloud Data Catalog, DLP, and IAM policies. Adept at collaborating with cross-functional teams including data scientists, DevOps engineers, and business analysts to deliver data-driven solutions and actionable insights. Skilled in building interactive, real-time dashboards and BI solutions using Tableau, Power BI to support strategic decision-making.