Patrick Muller is a Senior Data Engineer with two decades of IT experience and nine years focused on big data and analytics, currently helping AWS customers extract business value from large-scale datasets as part of the Data Lab team. He combines deep operational expertise in maintaining complex clusters and production troubleshooting with hands-on coding to automate maintenance, monitor performance, and build data solutions. A subject-matter expert in Presto/Trino, Athena, and Glue, he also has advanced experience across EMR, Hadoop, Spark, Kafka, DynamoDB and MongoDB, enabling end-to-end analytics architectures for streaming and batch workloads. Patrick contributes to notable open-source work such as enhancements to aws-sdk-pandas for Glue catalog integration, reflecting practical integration experience with AWS data lakes. He has a background in middleware and infrastructure at scale, plus leadership experience mentoring engineers, running interviews, and communicating metrics to stakeholders. Based in Virginia, he blends systems reliability instincts with applied ML and blockchain knowledge to deliver pragmatic, production-ready analytics solutions.
9 years of coding experience
4 years of employment as a software developer
Faculty of Science, Mathematics and Computer Science, Faculty of Science, Mathematics and Computer Science at Uniandrad
Graduate Course, Big Data, Graduate Course, Big Data at Universidade Positivo
pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
Role in this project:
Data Engineer
Contributions:11 commits, 11 PRs, 5 pushes in 8 months
Contributions summary:Patrick primarily contributed to the `aws/aws-sdk-pandas` repository by implementing and testing features related to data catalog integration, specifically for AWS Glue. Their work involved creating and deleting databases within the AWS Glue Catalog and adding these functionalities in the notebook. The user also focused on refining the code by addressing validation issues and adding wait objects in test cases. These changes showcase expertise in interacting with AWS services and enhancing data lake functionality.
Pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
Contributions:2 pushes, 8 branches in 1 year 6 months
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