Amanda Lambert is a strategic HR operations leader with over 20 years of experience designing and scaling global HRIS, payroll and service delivery models that align people systems to business goals. She has led large SAP SuccessFactors implementations and consolidations that cut administrative costs, improved data quality, and delivered real-time HR analytics for executive decision-making. Known for building high-performing teams, Amanda has driven measurable productivity gains through process standardization, centralized employee support, and continuous improvement across payroll, global mobility and HR systems. Her background combines deep payroll and HRIS technical know-how with change management and data-driven governance, enabling smooth integrations during M&A and system migrations. Uncommonly for an HR executive, she also contributes to open-source backend projects (including Apache Flink and BookKeeper), reflecting a practical engineering mindset and attention to operational reliability. Based in Houston, she partners with executives as a trusted advisor to translate complex HR technology initiatives into operational impact.
Upserts, Deletes And Incremental Processing on Big Data.
Role in this project:
Back-end Developer
Contributions:28 reviews, 100 commits, 124 PRs in 1 year 3 months
Contributions summary:Amanda primarily worked on the `apache/hudi` project, a data lake framework. Their contributions focused on refactoring code based on new import order rules. This involved changes across multiple modules like `hudi-client`, `hudi-hive`, `hudi-hadoop-mr`, and utilities, indicating a broad understanding of the project's architecture. The user also addressed specific issues, such as fixing NPE errors in the CLI and refining code related to savepoints, implying a focus on stability and usability.
Contributions:27 commits, 44 PRs, 74 comments in 11 months
Contributions summary:Amanda primarily focused on bug fixes and code improvements related to data caching and complex dimensions within the Apache CarbonData project. They addressed issues like incorrect field naming conventions, missing complex dimensions during data preparation, and optimization of property file reading. Additionally, the user contributed to checkstyle improvements, enhancing code quality and adherence to coding standards. These contributions indicate a focus on core data processing logic and code maintainability within the system.
carbondatadata-formatdata-storeapachebig-data
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