Jennifer Lu

Jennifer Lu

Principal Data Engineer @ Barracuda Networks

About Jennifer Lu

Jennifer Lu is a Principal Data Engineer at Barracuda, with extensive experience in data engineering and software development.

Company

Jennifer Lu is currently working at Barracuda, where she holds the position of Principal Data Engineer. Barracuda is headquartered in San Francisco, California, and Jennifer works remotely from her location. She previously worked for Barracuda as a Data Platform Engineer for two years, based in Campbell, California. Her extensive work in various capacities at notable companies showcases a robust career in data engineering and development.

Title

Jennifer is the Principal Data Engineer at Barracuda. Her role involves leading data engineering projects and ensuring the efficiency and effectiveness of the organization's data initiatives. With her background in data science and engineering, she brings extensive experience to this senior position.

Education and Expertise

Jennifer Lu studied at Virginia Tech, where she achieved a Bachelor of Applied Science (B.A.Sc.) in Computer Engineering. Her expertise includes designing sophisticated MLOps pipelines, building ETL pipelines, and optimizing PySpark pipelines in Databricks. She is proficient in using advanced tools like MLFlow, ElasticSearch, Terraform, DLT, and PowerBI.

Professional Background

Jennifer's professional journey includes diverse roles across several prestigious companies. She worked at Castlight Health as a Senior Data Science Engineer for six years, followed by a role at Oracle as an Application Developer for two years. Her earlier career also includes positions at DataRaker, Inc. as a Software Developer, and Media Matters for America as both a System Developer and Director of Technology. These roles have equipped her with extensive experience in software development, system management, and data engineering.

Key Projects and Initiatives

Throughout her career, Jennifer has been involved in crucial projects and initiatives. She designed an MLOps pipeline to handle tasks like pre-computing features, automating model scoring and training, and storing data in Databricks Feature Store. She also built ETL pipelines to support sales dashboards using tools like Terraform, DLT, and PowerBI. Additionally, Jennifer developed a labeling pipeline integrating an MLFlow model for auto-labeling and utilized ElasticSearch for data storage, optimizing these processes for enhanced performance and cost-efficiency.

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