Chris Caspanello is a Senior Software Engineer with 11 years of experience building reliable, production-grade systems across enterprise and big-data domains. He has modernized legacy stacks into Spring Boot microservices, led cloud and CI/CD migrations, and driven feature work for customer-facing apps at organizations like Disney Cruise Line, Hitachi Vantara, and OverOps. Comfortable across backend, DevOps and frontend boundaries, he’s implemented multi-tenant analytics on AWS QuickSight, integrated big-data engines (Spark, Beam) for Pentaho, and contributed UI and maintainability improvements to widely used Pentaho projects. Chris combines a pragmatic focus on observability and deployment automation with a knack for refactoring legacy codebases into testable, documented services—often documenting and executing full migration plans. Based in Orlando, he brings a consistent track record of shipping measurable reliability improvements for large enterprise customers.
11 years of coding experience
15 years of employment as a software developer
B.A Information Technology, B.A Information Technology at University of Central Florida
Contributions:112 commits, 178 PRs, 65 pushes in 3 years 7 months
Contributions summary:Chris primarily contributed to the Pentaho Data Integration (PDI) project, also known as Kettle. Their work included fixing SWT icon breakages, adding null checks and formatting in the Validator class, and deprecating unused methods. Furthermore, they implemented methods to support stopping transactions and added a password field to the ExcelInput dialog. They also addressed issues within the file input and output components.
Kettle plugin that provides support for interacting within many "big data" projects including Hadoop, Hive, HBase, Cassandra, MongoDB, and others.
Role in this project:
Back-end Developer
Contributions:17 commits, 38 PRs, 24 pushes in 3 years 7 months
Contributions summary:Chris's primary contribution focused on enhancing the user interface for the Hadoop input step within the Pentaho Big Data Plugin. They refactored code, moved variables closer to implementation for better encapsulation, and introduced constants for redundant string usage. Additionally, the user converted code to use lambda expressions and diamond operators, indicating efforts to modernize and improve the codebase's readability and maintainability. These changes involved modifying the HadoopFileInputDialog Java class, likely impacting data input functionality.
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