Veda Nadendla

Seasoned Azure Data Engineer with 7+years of experience in designing, developing, and maintaining scalable data pipelines using Azure cloud services to ensure reliable and efficient data processing. Developed and optimized ETL/ELT workflows using Azure Data Factory, Azure Synapse Analytics, and Azure Databricks for large-scale data integration. Documented data integration processes, data mappings, and transformation logic to maintain data lineage and facilitate knowledge sharing across teams.

Developed and maintained SQL scripts for database performance monitoring, query optimization, and bottleneck analysis. Implemented interactive report filtering, cascading parameters, and default parameter values in SSRS to enhance user experience and reporting flexibility.

Developed

Python scripts using Pandas and NumPy for data cleansing, transformation, validation, and automation tasks. Collaborated with data architects and business stakeholders to design and implement enterprise data warehouse solutions. Designed and implemented automated data refresh schedules for Power BI reports and dashboards, ensuring timely and accurate business insights.

Implemented data retention, archival, and lifecycle management strategies in Azure Synapse Analytics to optimize storage utilization and compliance requirements.

Integrated Azure

Databricks with metadata management and data cataloging tools to improve data discoverability and governance. Configured SSIS package checkpoints, restart ability, and error-handling mechanisms to improve ETL reliability and fault tolerance. Created and maintained comprehensive technical documentation for databases, data warehouses, ETL processes, and reporting solutions.

Designed and implemented robust Azure Data Lake Storage architectures with Bronze, Silver, and Gold data zones for efficient data organization and processing. Developed and maintained enterprise data governance frameworks to improve data quality, compliance, security, and operational efficiency. Built and managed SSAS data mining models and analytical solutions to derive actionable business insights.

Utilized

Git version control for source code management, branch management, code reviews, and deployment tracking. Collaborated with cross-functional teams to gather business requirements and translate them into scalable data models and BI solutions.

Developed

Power BI dashboards and visualizations to support strategic decision-making and business performance monitoring. Implemented role-based security and data access controls across Power BI, Azure Synapse, and Azure SQL environments. Optimized data processing performance through partitioning, indexing, parallel execution, and query tuning techniques. Supported CI/CD implementation using Azure DevOps pipelines for automated deployment and version management of data engineering solutions.

Azure Data Engineer at Unit4 (2025-02 – Present)

• Configured Azure Blob Storage lifecycle management policies and access tier migration strategies to optimize storage costs and improve data accessibility.
• Developed and maintained automation scripts using PowerShell, Python, and Azure CLI to streamline administrative and data engineering tasks within Azure Synapse Analytics.
• Collaborated with application development teams to optimize SQL queries, stored procedures, and database objects, improving application performance and reducing query execution times.
• Designed and implemented end-to-end data integration solutions using AWS Glue, integrating seamlessly with Amazon S3, Amazon Redshift, and Amazon Athena.
• Partnered with data engineers, analysts, and business stakeholders to design and implement scalable data models in Snowflake, supporting enterprise reporting and analytics requirements.
• Developed and optimized dimensional and relational data models in Amazon Redshift to support business intelligence and data warehousing initiatives.
• Created and maintained data models in Google BigQuery, enabling efficient reporting, analytics, and large-scale data processing.
• Collaborated with business users and stakeholders to gather requirements, design dashboards, and develop interactive QlikView applications for data-driven decision-making.
• Worked closely with data scientists and analysts to identify trends, patterns, and business insights using Python and Plotly-based visualizations.
• Automated data engineering workflows using Azure Data Factory, Azure Synapse Pipelines, and scripting solutions to improve operational efficiency and reduce manual effort.
• Configured custom metadata classifications and search facets within Azure Purview to enhance enterprise data discovery, governance, and cataloging capabilities.
• Implemented Azure Key Vault security best practices, including Privileged Identity Management (PIM), for secure and time-bound access to sensitive resources.
• Utilized Azure Cosmos DB diagnostics and monitoring tools to analyze query performance, troubleshoot issues, and optimize resource utilization.
• Developed machine learning and natural language processing solutions leveraging transfer learning techniques and pre-trained models for domain-specific analytical requirements.
• Established Git repository standards, including branching strategies, code review processes, and commit message guidelines to improve collaboration and code quality.
• Collaborated with data architects to design and implement data virtualization solutions, enabling unified access to distributed data sources.
• Integrated Power BI with Azure Machine Learning services to deliver predictive analytics and advanced forecasting capabilities.
• Leveraged Power BI AI features, including Key Influencers and Smart Narratives, to uncover significant business drivers and data trends.
• Designed and implemented database backup, recovery, and disaster recovery procedures to ensure business continuity and data protection.
• Configured Azure Data Factory triggers, schedules, and event-driven pipelines to automate and orchestrate enterprise data integration processes.
• Implemented data encryption and security controls to safeguard sensitive data at rest and in transit, ensuring compliance with organizational security standards.
• Developed and maintained DAX measures, calculated columns, KPIs, and business metrics in Power BI to support executive reporting and performance monitoring.
• Managed Azure DevOps repositories and implemented branching, merging, and CI/CD strategies to support collaborative development and controlled deployments.
• Developed ETL workflow scheduling, dependency management, and monitoring frameworks to ensure reliable and efficient data processing.
• Optimized data storage, ingestion, and query performance using Azure Databricks Delta Lake architecture and best practices.
• Developed Python-based solutions to extract, transform, and load data from RESTful APIs and external data sources into enterprise data platforms.

Power BI and Azure Specialist at Accel Frontline (2022-03 – 2025-01)

• Implemented and managed data transformation, cleansing, and enrichment processes within Azure Data Lake, ensuring high-quality and reliable datasets for analytics and reporting.
• Developed and maintained scalable data models and ETL workflows using AWS Glue to support business intelligence, reporting, and analytical initiatives.
• Integrated Snowflake with business intelligence platforms such as Tableau and Power BI, enabling efficient data visualization, self-service analytics, and enterprise reporting.
• Connected Amazon Redshift with Tableau and Power BI to facilitate interactive reporting and advanced data analysis.
• Implemented Google BigQuery integrations with Power BI and Tableau, providing scalable analytics and visualization capabilities for business users.
• Conducted user training sessions and knowledge transfer workshops on QlikView applications, promoting user adoption and self-service analytics capabilities.
• Optimized Plotly-based dashboards and visualizations to improve rendering performance, responsiveness, and overall user experience.
• Utilized Git version control for source code management, branch maintenance, code reviews, and release tracking across data engineering projects.
• Designed and implemented dimensional and relational data models in Power BI, ensuring data accuracy, consistency, and optimized reporting performance.
• Managed SSIS event handlers, notifications, logging mechanisms, and error-handling frameworks to improve ETL monitoring and operational reliability.
• Configured and automated Power BI dataset refresh schedules to ensure reports and dashboards consistently reflect the latest business data.
• Implemented Azure Blob Storage archival and lifecycle management strategies to optimize storage utilization and reduce operational costs.
• Collaborated with business stakeholders to gather reporting requirements and deliver actionable insights through interactive Power BI dashboards and reports.

Bachelor of Technology – Audisankara college of Engineering and Technology