BHANU PRAKASH

Data Engineer with 6+ years of experience in architecting, developing, and managing scalable data pipelines and distributed data processing systems across cloud platforms including Azure, AWS, GCP, and hybrid environments. Strong experience in building robust ETL/ELT pipelines using tools such as Azure Data Factory, Apache NiFi, Airflow, Informatica, Talend, and dbt, processing both structured and semi-structured data from diverse sources including APIs, flat files, RDBMS, and NoSQL databases. Designed and implemented data lake and warehouse solutions using Azure Data Lake Storage Gen2, Synapse Analytics, Redshift, and BigQuery, adhering to medallion architecture principles (Bronze, Silver, Gold layers). Deep expertise in SQL development (T-SQL, PL/SQL, PostgreSQL) and Python scripting for data transformation, automation, and orchestration of complex workflows in both batch and streaming environments. Built scalable and performant data integration pipelines for ingesting high-velocity streaming data using Apache Kafka, AWS Kinesis, and Azure Event Hubs, enabling real-time analytics and monitoring solutions. Hands-on experience with data modeling techniques (Star, Snowflake, 3NF), developing and maintaining fact/dimension tables, materialized views, surrogate keys, and surrogate key handling for SCD implementations. Successfully led data migration projects from on-premise systems such as Teradata, SQL Server, Oracle, Netezza to cloud-native platforms (Snowflake, Redshift, Synapse) using Python, SnowSQL, DMS, Striim, ensuring data quality and performance. Skilled in performance tuning and optimization of SQL queries, partitioning strategies, indexing, clustering, and efficient data storage practices to enhance processing efficiency and reduce cost. Experience implementing data governance, metadata management, and security controls including RBAC, OAuth, Key Vault, encryption (KMS), and Dynamic Data Masking in compliance with regulatory requirements. Developed automated CI/CD pipelines using GitHub Actions, Jenkins, and Terraform to deploy data artifacts and monitor data pipeline health in production environments. Collaborated with cross-functional teams including Data Scientists, BI Analysts, and DevOps Engineers to deliver high-quality data assets supporting machine learning, BI dashboards (Power BI, Tableau), and real-time alerts. Experience in building metadata-driven frameworks for reusability, error handling, logging, and auditability of ETL jobs, reducing manual intervention and enhancing pipeline reliability. Familiarity with ML/AI integration into pipelines and deploying models using Databricks, Azure ML, and REST APIs, and monitoring inference performance. Proficient in Unix Shell, Python, and SQL scripting for batch automation, file manipulation, and workflow triggers in production ETL workloads. Exposure to enterprise-scale environments, supporting hybrid cloud architectures and multi-tenant data warehouses, with strong documentation and stakeholder communication skills.