Murali Nandi

• With over 5 years of experience in software development, expertise spans both backend and frontend development, specializing in Java and J2EE technologies with a strong understanding of Object-Oriented Design concepts and analysis.
• Proficiency in different phases of the Software Development Life Cycle (SDLC) and experience working with Agile and Waterfall methodologies ensure adaptability in diverse project environments
• Extensive experience in developing User Interface (UI) rich web applications and web service applications using modern frontend technologies, including Object-Oriented JavaScript, React.js, Angular.js, Node.js, Express.js, Bootstrap, and CSS frameworks.
• A strong foundation in micro services architecture is complemented by hands-on experience with Docker and Kubernetes for managing distributed cluster components.
• In addition to software development, specialization as a Data Engineer includes building and managing data pipelines, ETL processes, and data warehousing solutions while ensuring high data quality.
• Strong expertise in Microsoft 365 and AWS, GCP and Azure Cloud enables efficient cloud-based solutions for scalable data management.
• Technical skills also include J2EE testing, developing test cases using JUnit and Mockito, and implementing logging with Log4j.
• Proficiency in version control, build automation, and CI/CD tools such as Git, GitHub, SVN, Bitbucket, Ant, Maven, Gradle, and Jenkins ensures seamless development and deployment workflows.
• Experience in Microservices, Service Oriented Architecture (SOA) with XML-based Web Services (SOAP/REST).
• A comprehensive skill set in application development and data engineering supports the creation of scalable, high-performance solutions that drive business success.

Collaborated with Product Owners and stakeholders to develop the technical architecture for a Data
Quality project, ensuring alignment with business and solution requirements.
Designed and implemented scalable ETL pipelines using Azure Databricks, Azure Data Factory
(ADF), and PySpark for efficient data movement.
Ingested structured and unstructured data from Azure Data Lake Storage (ADLS), SQL Server,
CosmosDB, and external APIs into Azure Synapse Data Warehouse (DWH).
Developed real-time and batch data pipelines using Kafka, Event Hubs, and Delta Lake for lowlatency data processing.
Optimized data partitioning and indexing in Apache Spark and SQL Server to improve query
performance.
Implemented change data capture (CDC) mechanisms in ADF to track incremental data loads.
Optimized Spark job performance by tuning configurations, caching strategies, and efficient
partitioning, reducing execution time by 40%.
Implemented monitoring dashboards using Datadog, PagerDuty, Azure Monitor, and Prometheus
to track pipeline health and performance.
Configured alerting mechanisms in ADF, Azure Log Analytics, and CloudWatch for proactive issue
resolution.
Automated error handling, logging, and retries in ADF and Databricks Workflows for robust
pipeline execution.
Developed data models in Power BI, Tableau, and Looker for insightful reporting and analytics.
Integrated with REST APIs, SAP HANA, and third-party data sources to enrich datasets.
Enabled near real-time reporting using Synapse Link and Kafka Streams.
Version-controlled SQL scripts, ETL configurations, and transformations using dbt and Git.
 

Master’s in Computer Science| University of North Texas| Denton, TX
Bachelor’s in Computer Science | Jawaharlal Nehru Technological University| Hyderabad, India