Matt McMahon

About Matt McMahon

For the previous 3 years I have been engaged with the AEO Data Engineering team working to build out the standards and environmental architecture for the new GCP platform. This role included work on policy, standards, data movement, pipelines, file movement, orchestration, ETL,ELT, table & view architecture, and orchestration along with many other focus areas. Additionally I worked on continued support and enhancement of the existing Teradata EDW including work with Informatica, Shell Scripting,ETL, SQL Debugging, Cognos and OnCall responsibilities. The current Teradata EDW was set to migrate to the GoogleCloudPlatform and I worked closely with third party vendors on the migration from Teradata to BigQuery which included every table, script and report from our current environment to a fully new platform. Within the Google Cloud environment I worked extensively on creating DataFusion pipelines and DAGs to be orchestrated through Airflow via Google CloudComposer.

Experience

1. Google Cloud Platform Data Engineer

   American Eagle Outfitters Inc.

   Sep 2019 — Present · Pittsburgh, PA, US

   DataFusionFor DataFusion, we were an early adopter, and as such, were responsible for assisting in the discovery of, and debugging of issues within many of the primary data connection, manipulation, and storage plugins. The multiple different systems of connection were each unique and had different challenges associated with the initial setup and configuration. Through this tool we were able to provide consistent and reliable pipelines of data from the source systems to the cloud for immediate consumption into a foundational data layer. Connected data systems include DB2, Oracle, Teradata, MySQL, Google Cloud Storage buckets, third party SFTP, and others.Cloud ComposerThis tool was initially one of the primary means of importing and translating the raw data from the different AEO source systems. These source systems included DB2, Oracle, Teradata, MySQL, Google Cloud Storage buckets, third party SFTP sites and others. The DAGs were built by utilizing native operators via Python Code in and custom developed operators specific to AEO code bases. Through these operators we were able to handle many different data ingestion issues to produce consistent and reliable data to the end consumer. Once direction shifted to using Data Fusion as the primary means of Data extraction, we pivoted to make Composer our main means of ETL using current and just loaded BQ tables. Additionally, Composer became the tool we used for orchestration and predecessor/successor management on a process work flow. Through Composer we were able to easily handle a dynamic, multi-faceted orchestration and scheduling tool with data extraction capabilities.CloudFunctionsEach of these methods of moving data from on-premises data stores to the cloud were set to be orchestrated through various CFs within the Google cloud environment. These CFs were developed in Python and deployed via code management. The CFs were able to be triggered by multiple different methods

Education

• Duquesne University

  Bachelor's degree, Information Technology

  1998 — 2002

• Duquesne University

  Bachelor's degree, Information

  1998 — 2002