Sergio Peña
Staff II Software Engineer at Confluent
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolSergio Peña is a Staff II software engineer with 11 years of experience building distributed systems, batch and streaming SQL databases, and big-data platform services from Austin, Texas. He is a technical owner and cross-team integrator at Confluent, leading the SQL Metadata Service and driving Flink batch query support for Confluent Cloud. Sergio’s contributions to prominent open-source projects—Apache Flink, ksqlDB, and Kafka—focus on SQL/table planning, parser work, UDF/UDAF correctness, and optimized RocksDB-based window stores, reflecting deep expertise in storage schemas and stream processing. He combines hands-on backend engineering with mentoring and technical leadership, coaching engineers across seniority levels. Notably, he has a background in security and low-level systems from earlier roles at Cloudera and Gazzang, giving him a rare blend of database, streaming, and kernel-level experience. Colleagues rely on him to clarify complex designs and deliver reliable, integrated services across cloud and open-source ecosystems.
11 years of coding experience
17 years of employment as a software developer
Bachelor Computer Systems, Bachelor Computer Systems at Universidad Autónoma de Sinaloa
English, Spanish
Github Skills (19)
parser10
apache-flink10
testing10
rocksdb10
big-data10
kafka10
flink-sql10
java10
scala10
javas10
udf10
sql10
ksqldb10
stream-processing10
test-automation10
Programming languages (6)
Github contributions (5)
confluentinc/ksql
Jan 2019 - Aug 2022
The database purpose-built for stream processing applications.
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:690 reviews, 256 commits, 405 PRs in 3 years 8 months
Contributions summary:Sergio's contributions primarily focus on enhancing the ksqlDB database's functionality and reliability. They implemented test cases to verify the correct registration of built-in User-Defined Functions (UDFs) and User-Defined Aggregate Functions (UDAFs), particularly ensuring uppercase registration. The user also refined string masking UDF parameter handling by moving shared logic to a dedicated class and adding a new UDF for string splitting. Moreover, they added a command-line parameter to the QueryTranslationTest and SchemaTranslationTest to specify and execute tests from a comma-separated list of JSON files.
materialized-viewsstream-processingstreaming-queriesksqldbsql
apache/kafka
Apr 2021 - May 2021
Mirror of Apache Kafka
Role in this project:Back-end Developer
Back-end DeveloperContributions:79 reviews, 6 commits, 8 PRs in 29 days
Contributions summary:Sergio primarily focused on enhancing the Apache Kafka codebase by implementing and refactoring state stores, particularly RocksDB-based window stores. They developed a new RocksDBTimeOrderedWindowStore to optimize time-based range queries, including modifying the underlying key schema to allow for efficient duplicate record deletion. Their contributions also involved fixing stream-stream join issues, with associated code changes. The user's work reflects a focus on optimizing data storage and retrieval within the Kafka streaming framework.
apache-kafkakafka-consumerapachescalakafka
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