Native Ads

• SME on Targeting and Eligibility for Native Ads

• Optimization of data pipelines resulting in 20% of reduction on cloud computing costs for the team.

• Re-engineered 100+ pipelines from daily to hourly cadence to support shorter SLAs

• Implementation of backend services in Java to provide data to Front-End applications

• Currently responsible for ~125 data pipelines implemented in Scala and running on GCP

• Data Engineering tech lead of the team, mentoring junior engineers, prioritizing projects and tasks, and guiding design choices

Lyft Maps

• Design and implementation of data pipelines in Scala/Spark on AWS to support Machine Learning applications.

• SME of data pipelines that processed Routing and ETA of rides

Fun Fact: fixed data issue that in some scenarios would result in Lyft cars being routed through back alleys, parking lots, gas stations as a shortcut.

2015 to 2020 - BingAds

• Development of data pipelines using Cosmos and Azure to analyze application logs that enabled the team to identify the source of performance issues.

• Development of PowerBI dashboards to support the reporting of performance and reliability of the service.

• Re-engineering of Data Processing pipeline to compute Bing Ads revenue, using Azure Data Lake Analytics and Azure Data Factories, dramatically improving reliability and reducing cloud computing costs.

2020 to 2022 - Azure Intelligence Platform

• Re-engineering and migration of hundreds of data pipelines into Azure Data Factory V2 and Azure Data Lake Analytics to handle the deprecation of Azure Classic services.

Substitute Professor - Databases II; Programming Languages

Research in compilers, with focus in code analysis and optimization. Project E-CoSoc