Shardul Mahadik is a Staff Software Engineer based in the Greater Seattle Area with a decade of experience building resilient back-end systems and infrastructure at LinkedIn. He has deep expertise in large-scale data processing and query engines, contributing to high-profile open-source projects like Apache Spark, Apache Iceberg, and LinkedIn's Coral where he improved dependency handling, serialization, and SQL translation logic. His background spans systems engineering, backend development, and ETL work—from hedge-fund back-office systems to cloud-native data platforms—demonstrating strong reliability and interoperability instincts. At LinkedIn he progressed from systems engineering to staff-level ownership, consistently shipping fixes and performance improvements that reduce failure modes in distributed environments. He holds an MS from Carnegie Mellon and brings a methodical, test-driven approach to refactoring and type-safe data handling across complex codebases. Colleagues value him for pragmatic architectural decisions and the uncommon combination of production-grade systems experience with hands-on open-source contributions.
10 years of coding experience
8 years of employment as a software developer
Bachelor of Engineering (BE), Computer Engineering, 77.81%, Bachelor of Engineering (BE), Computer Engineering, 77.81% at University of Mumbai
Master of Science (MS), Information Technology, 3.95, Master of Science (MS), Information Technology, 3.95 at Carnegie Mellon University
Coral is a translation, analysis, and query rewrite engine for SQL and other relational languages.
Role in this project:
Back-end Developer
Contributions:10 reviews, 7 commits, 3 PRs in 2 years 7 months
Contributions summary:Shardul primarily contributed to the core functionality of the Coral translation and query rewrite engine, as seen in the code modifications. Their work includes implementing support for Hive BIGINT literals, fixing LIMIT clause issues, and adding new functions like `from_unixtime` to the Presto UDF map. The commits also show efforts in refactoring and improving data type handling within the system, specifically addressing data type rewrites for Presto, demonstrating a focus on code quality and system interoperability.
Contributions:95 reviews, 32 commits, 53 PRs in 1 year 5 months
Contributions summary:Shardul contributed to Apache Iceberg by implementing core features and addressing issues related to data processing and storage. They focused on improving the robustness of the system by handling exceptions and supporting different data types like Avro enums. The user also worked on performance enhancements by adding JMH tests and refactoring reader code. Their contributions are centered around ensuring the efficient and correct handling of data within the Apache Iceberg framework.
apache-icebergapachebig-datadatastreamjava
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