Murali Pole

Azure Data Engineer with 3 years of experience building scalable ETL/ELT pipelines and cloud-based data platforms using Azure Data Factory, Databricks, PySpark, Delta Lake, and Azure Synapse Analytics. Experienced in large-scale data transformation, warehousing, CI/CD automation, and supporting analytics and AI/ML workloads on Azure. Strong foundation in software engineering, DevOps, and cloud infrastructure/security fundamentals.

Job Title: Data Engineer

• Developed ETL/ELT pipelines using Azure Data Factory and Azure Databricks to ingest and transform structured and semi-structured datasets processing 500K+ records per day across multiple data sources.
• Built PySpark transformation workflows for cleansing, schema validation, deduplication, joins, and aggregations across 10+ ingestion pipelines using Delta Lake Bronze, Silver, and Gold architectures.
• Created reusable metadata-driven ADF pipelines using Lookup, ForEach, Metadata, and Copy activities, reducing repetitive pipeline development effort and improving onboarding speed for new datasets.
• Optimized Azure Synapse Analytics queries using partitioning, indexing, SQL tuning, and schema improvements, reducing reporting query execution time by approximately 30% for analytics workloads.
• Automated CI/CD deployment workflows for ADF pipelines and Databricks notebooks using Azure DevOps across development, staging, and production environments, improving deployment consistency and reducing manual release effort.
• Configured secure Azure access patterns using VNets, NSGs, RBAC, Managed Identities, and Azure Bastion to reduce credential exposure and strengthen cloud security practices.
• Developed SQL transformation logic, validation queries, and warehouse optimization workflows supporting reporting and analytics use cases across multiple business datasets.

Job Title: Frontend Software Engineer

• Developed and maintained enterprise Angular applications and integrated REST APIs with backend services.
• Automated build and deployment workflows using Docker and GitHub Actions, improving deployment consistency and reducing release effort.
• Improved frontend performance and asset optimization workflows, reducing application load times by approximately 40%.

M.Sc. in Artificial IntelligenceInstitution: Moscow Institute of Physics and Technology (MIPT)Dates: 2024 – Expected 2027 (Academic Leave)

B.Tech. in Computer ScienceInstitution: University of MysoreDates: 2015 – 2019