Prasad D
Azure Data Engineer @NTT Global Data Centers
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WORK HISTORY
Azure Data Engineer @NTT Global Data Centers
Developed and maintained ELT pipelines using Microsoft Fabric Data Pipelines, Azure Data Factory (ADF v2) and Azure Databricks to ingest and transform structured and semi-structured data from multiple enterprise sources. Implemented Fabric Lakehouse and OneLake storage solutions to consolidate datasets into a unified analytical layer, supporting scalable reporting and improved Power BI performance. Worked with analytics teams to integrate Fabric Dataflows Gen2 with Power BI Direct Lake mode, enabling near real-time reporting and self-service BI capabilities. Configured Fabric governance features, including Unity Catalog, to support data access controls, lineage visibility and compliance requirements. Built and maintained Delta Lake based Lakehouse solutions with ACID compliance, Slowly Changing Dimensions (SCD) and time-travel features to support historical data analysis. Developed parameterized and metadata driven Fabric and ADF pipelines to support consistent and reusable data ingestion patterns across datasets. Created reusable PySpark notebooks using Python, Pandas and NumPy to perform data cleansing, transformation, and aggregation for analytics use cases. Implemented streaming data pipelines using Azure Event Hubs, Azure Stream Analytics and Apache Kafka to support near real-time data ingestion and monitoring scenarios. Enhanced Power BI reporting performance by implementing composite models, dual storage modes and Row-Level Security (RLS) for secure analytics access. Developed Power BI dashboards with interactive visuals, slicers, and drill-down capabilities to support business reporting and data exploration. Implemented data validation and quality checks using Python based frameworks and ADF activities to identify data inconsistencies and improve pipeline reliability. Supported CI/CD processes for data pipelines and analytics assets using Azure DevOps YAML pipelines, GitHub Actions and ARM/Bicep templates to automate deployments.
EDUCATION
Vasavi junior college
Intermediate, MPC
Birmingham City University
Master's degree, Computer Science
Vivekanandha high school
SSC
Bapatla Engineering College
Bachelor's degree, Electronics and Communications Engineering
ABOUT PRASAD D
Azure Data Engineer with 5 plus years of experience designing building and supporting scalable cloud data platforms on Microsoft Azure and Microsoft Fabric. I specialize in developing end to end data solutions that enable analytics reporting and data driven decision making across enterprise environments. My core strengths include Azure Data Factory Microsoft Fabric Databricks PySpark Synapse Analytics and Power BI with strong focus on performance reliability and data governance.I have hands on experience building batch and real time data pipelines using ADF Fabric pipelines Databricks and Spark along with streaming solutions using Event Hubs Kafka and Stream Analytics. I work extensively with Lakehouse and Data Warehouse architectures using OneLake and Delta Lake with Bronze Silver and Gold layer design patterns to deliver trusted and analytics ready datasets. I also build reusable ELT frameworks using PySpark SQL DBT and Airflow to support automated and modular data processing.My experience includes implementing CI CD for data platforms using Azure DevOps GitHub Actions Terraform and Bicep along with secure access controls using Azure AD RBAC Managed Identities and Key Vault. I focus on data quality validation monitoring and lineage using Azure Monitor Application Insights and Purview to ensure trustworthy and well governed data assets.I regularly collaborate with analytics teams product owners and business stakeholders in Agile environments to translate requirements into efficient data models semantic layers and Power BI dashboards with Direct Lake DAX and row level security. I am certified in Microsoft Fabric Data Engineering and continuously work on improving platform performance cost efficiency and scalability through optimization and automation.
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