Vannel Zeufack Email & Phone Number

Atlanta, GA, US

Alpharetta, Georgia, United States

• Leads a team of 3 to build and maintain quality data pipelines.
• Work closely with client facing team to gather requirements, set project timelines and support all clients (10+) data ingestion needs.
• Collaborate with product team for sprint planning and project tracking on Zoho.
• Collaborate with data science team to provide actionable customer data to support analytics efforts.
• Conducted 40+ technical interviews which allowed to fill 5+ roles in the ETL and data science teams.
• Championed thorough code reviews within the team, resulting in improved codebase quality.

Alpharetta, Georgia, United States

• Designed, implemented, and maintained 30+ ETL pipelines across all customers using ECL (SQL like language), HPCC Systems, Kafka and ELK, allowing a smooth intake of millions of transactions daily.
• Increased data quality by 15% through data validation and cleaning.
• Reduced the overall ingestion time by 30% by identifying and optimizing resource consuming jobs through optimal use of indices.
• Enhanced code quality by implementing clean code standards and organization leading to a 40% decrease in total lines of code required for an ETL, fewer bugs and faster code reviews.
• Created training documentation used to train all new ETL developers.
• Implemented data masking on personal identifying data to ensure client’s data security.

United States

• Used ECL to develop a machine learning preprocessing bundle, aimed at facilitating the data preparation phase of machine learning projects developed on the HPCC Systems platform.
• The bundle included 7 modules: label encoding, one hot encoding, standard scaling, min-max scaling, normalization, percentage-based data split and stratified data splitting.
• The module adhered to clean code standards, including fixed line length, consistent indentation (2 spaces), appropriate comments, and proper naming conventions. It underwent successful functional and performance.

• Developed a K-Means based anomaly detection system for detecting potential abnormal users and potential abnormal periods of activity in a web server by analyzing the logs.
• For detecting abnormal users, 7 features including average daily requests, number of active days and average size of requests were extracted. The users were then clustered to flag abnormal ones.
• For detecting abnormal periods of activity, the logs were divided into one-hour periods to determine the count of requests per period. Periods were then clustered, and abnormal ones were flagged.
• 3rd place winner at the Summer 2019 HPCC Systems Poster Competition

Master In Computer Science, Computer Science, 4.0

Bachelor Of Science - Bs, Computer Science, 3.83

Activities and Societies: Peer mentor for International Peer mentor program and the Odyssey Program