Pooja Nilangekar is a PhD candidate at the University of Maryland with 11 years of experience building scalable, distributed data management systems. Her industry work spans Google Spanner and Apache Impala, where she focused on backend stability and precise memory estimation to improve large-scale query execution. She has contributed to high-profile open-source projects like Apache Impala and Peloton, adding debugging clarity to Kudu scanner errors and refining planner memory accounting. Prior roles include research and engineering positions at Cloudera, CMU, Microsoft Research, and Snowflake, blending production systems engineering with academic research. She brings a rare combination of hands-on optimization (e.g., I/O-reducing Parquet column indexes) and formal algorithmic insight from distributed graph processing research. Based in College Park, MD, she pairs deep systems expertise with a demonstrated track record of shipping measurable performance improvements in both open-source and corporate environments.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Maryland
Bachelor of Engineering (B.E.) Computer Science & Engineering, Bachelor of Engineering (B.E.) Computer Science & Engineering at College of Engineering, Guindy
Master of Science in Information Networking (MSIN) Computer Systems Networking and Telecommunications, Master of Science in Information Networking (MSIN) Computer Systems Networking and Telecommunications at Carnegie Mellon University
Contributions:41 commits, 21 PRs, 187 comments in 5 months
Contributions summary:Pooja primarily focused on the database management system's code, with changes related to data structures like bloom filters and fixing issues related to the `StatementResult` data type. They addressed comments and made changes in the `statement.h` file, which suggests work on the query execution and result handling. The user also worked on resolving issues related to the `StatementResult` and modifying test files, indicating their involvement in debugging and improving the system's functionalities. Additionally, the user changed tests related to aggregate and order by SQL tests.
Contributions summary:Pooja primarily contributed to the Apache Impala project's backend by addressing issues related to Kudu scanner errors and improving memory estimates. The user added table name and node ID to Kudu scanner errors, which improved debugging. Furthermore, they worked on ensuring accurate memory estimates in the planner, accounting for the number of fragment instances, and improving memory usage estimations for aggregation nodes. These changes focused on enhancing the stability and efficiency of query execution.
apache-impalaparquetolapsqlapache
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