Sanjay Dornala

• Designed and implemented a hybrid cloud strategy integrating on-premises systems with Azure and AWS for secure financial data processing.
• Utilized Snowflake, Hadoop HDFS, Hive, and Spark for scalable storage, querying, and distributed processing.
• Automated data quality checks, integration, and transfers using Python, PL/SQL, and Apache Sqoop.
• Optimized performance with partitioning, bucketing, indexing, YARN resource allocation, and schema modeling (Star/Snowflake).
• Orchestrated and managed ELT workflows with Airflow, Oozie, Databricks, and Azure HDInsight.
• Automated infrastructure provisioning and deployments via Terraform, Ansible, Jenkins, and GitLab CI/CD.
• Implemented containerization and orchestration with Docker and AKS for scalable pipelines.
• Ensured data security and compliance through Azure AD, Key Vault, LDAP, encryption, SIEM, and audit logging.
• Deployed monitoring and alerting with Azure Monitor, AWS CloudWatch, Nagios, plus SNS/Notification Hubs for real-time incident response.
• Managed version control and collaboration with GitLab and Bitbucket, applying branching strategies and peer reviews.

• Designed and implemented end-to-end Hadoop data pipelines for ingestion, processing, and analysis.
• Migrated legacy SAS-based analytics to Spark, reducing processing time from 8 hours to 1 hour.
• Developed Java and Python-based ingestion frameworks to automate loading of batch data into Hive.
• Created in-house Java libraries using Hadoop APIs to streamline development processes.
• Built CI/CD pipelines using Jenkins, enabling automated testing and deployment.
• Led data migration from on-prem to cloud with AWS and Snowflake.
• Developed schema evolution techniques with Parquet to handle dynamic data structures.
• Designed real-time orchestration tools using YAML configuration to trigger event-based workflows.
• Technologies: Hadoop, Spark, HDFS, Hive, Java, Python, Snowflake, AWS, Oozie, Jenkins.

Project 1: Customer Risk Rating

• Automated regulatory reporting using Spark and Hadoop for all US Capital One credit card portfolios.
• Developed SCD Type 2 pipelines; data persisted in HBase and Hive.
• Migrated historical data from Oracle to HDFS and optimized performance for large-scale ETL jobs.

Project 2: Benefits and Values

• Developed real-time Kafka streaming platform to distribute customer data across domains.
• Built Scala-based file-to-Kafka application and offset tracking system using HBase.
• Transformed and published AVRO/JSON events to Kafka, supporting real-time web applications.

Project 3: Customer 360

• Aggregated customer, account, and transaction data into unified views.
• Enabled business intelligence teams to assess customer profitability using external bureau data (TransUnion, Experian, etc.).
• Created acquisition data pipelines to improve targeted marketing and promotions.
• Technologies: Hadoop, Spark, HDFS, Hive, Java, Python, Snowflake, AWS, Oozie, Jenkins.