Amar Reddy

Senior Cloud Data Engineer with 7+ years of experience designing, delivering, and supporting enterprise data platforms across AWS, Azure, and hybrid cloud environments. Proven expertise in leading cloud modernization initiatives, building scalable ETL/ELT pipelines, and developing Snowflake- and SQL-based data solutions that enable business intelligence, reporting, and enterprise analytics. Experienced collaborating with business stakeholders, product owners, architects, offshore teams, and QA to translate business requirements into reliable, production-ready data solutions while driving data quality, operational excellence, and continuous platform improvement.

Azure Data Engineer at DLL Finance (2024-11 – Present)

• Led the modernization of legacy IBM DataStage applications by delivering cloud-native ETL/ELT solutions using Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and Azure SQL.
• Designed and supported enterprise data pipelines that delivered trusted datasets for reporting, business intelligence, and downstream analytics.
• Built hybrid cloud architectures using Qlik Replicate to enable continuous Change Data Capture (CDC), ensuring uninterrupted reporting while legacy platforms were migrated.
• Collaborated with business stakeholders, solution architects, QA teams, and delivery teams to understand business requirements, validate data definitions, and deliver scalable technical solutions.
• Developed Python, Spark, and SQL-based transformation frameworks to improve pipeline maintainability, processing efficiency, and data consistency.
• Designed dimensional data models, including Fact and Dimension tables, supporting enterprise reporting and analytical workloads.
• Improved data quality by implementing automated validation, reconciliation, audit tracking, and Delta Lake ACID transaction capabilities.
• Participated in production support activities, including incident investigation, defect resolution, root cause analysis, pipeline optimization, and release validation.
• Established Azure DevOps CI/CD processes and SonarCloud quality standards to improve deployment reliability and maintain code quality across enterprise data solutions.
• Evaluated Microsoft Fabric and Azure OpenAI capabilities to enhance data engineering workflows, metadata management, and business-user analytics.

AWS Data Engineer at Wiley (2023-04 – 2024-10)

• Led the migration of legacy Informatica PowerCenter workflows into AWS Glue and Snowflake, delivering scalable cloud-based ETL solutions.
• Designed Snowflake data models and optimized SQL transformations to support enterprise reporting, business intelligence, and analytical workloads.
• Built automated ETL pipelines using AWS Glue, Amazon EMR, Snowpipe, Amazon S3, and Python to process batch, incremental, and CDC-based data.
• Worked closely with business analysts, product owners, QA teams, and architects to convert reporting requirements into reliable enterprise data solutions.
• Implemented automated data quality validation and monitoring using AWS Glue DataBrew, AWS Lambda, and Snowpark, improving production data reliability.
• Developed Tableau dashboards and analytical datasets supporting business users across multiple functional areas.
• Supported production deployments, investigated pipeline failures, optimized SQL performance, and resolved data integration issues to maintain operational stability.
• Automated infrastructure provisioning and release processes using Terraform, AWS CodePipeline, Git, and CI/CD best practices.

Data Engineer at John Dehere (2018-04 – 2021-12)

• Designed and developed ETL pipelines using Azure Data Factory (ADF v2), Azure Databricks, Azure Data Lake Storage Gen2, and Azure SQL Data Warehouse to integrate data from multiple banking systems.
• Developed reusable, parameter-driven Azure Data Factory pipelines that simplified onboarding of new data sources and improved maintainability.
• Built PySpark and Spark SQL transformation workflows in Azure Databricks to process high-volume transactional and customer datasets for enterprise reporting.
• Designed dimensional data models using Star and Snowflake schema methodologies to support business intelligence and analytical reporting.
• Implemented data validation, reconciliation, and quality checks to ensure data accuracy, consistency, and completeness across reporting datasets.
• Developed Power BI datasets, DAX measures, calculated columns, and interactive dashboards that enabled business users to analyze financial and operational KPIs.
• Collaborated with business analysts, QA teams, solution architects, and application teams to gather requirements, validate business rules, and deliver production-ready data solutions.
• Supported production ETL pipelines by troubleshooting failures, optimizing SQL queries, performing root cause analysis, and participating in deployment and release activities.
• Used Azure DevOps and Git for source control, sprint planning, work item tracking, and CI/CD pipeline deployments.