Data Engineer

BigData Development Boston, Massachusetts, United States

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Summary

Customized two automated ETL pipelines to get e-commerce websites performance data from 5 million daily users
Worked with stakeholders to understand their business needs and translated those into valuable data used for further analysis
Resolved misunderstandings regarding dataset structures, and missing records through presentations to stakeholders and other teams
Improved ETL pipeline of data coming from the Kanban Board of all the teams in the company to help manage and analyze available
resources effectively; Researched and enhanced LookML code for this data to help stakeholders plot various complex metrics
Implemented funnel to remove noise in daily data (e.g., Inflated Sessions) which helped improve data quality and accuracy
Investigated, fixed and documented changes in multiple SQL and LookML scripts to provide high accuracy, consistency and
reliability across different datasets and looker explores
Reduced time required to load the looker dashboard by implementing Aggregate tables and Caching in LookML

Expectations

Innovative and scientifically rigorous graduate student with significant data experiences to bring to the table. With a team-oriented attitude, I am eager to contribute my abilities in quantitative modeling and experimentation to enhance the experience of users around the world. Relevant skills include machine learning, problem-solving, programming, and creative thinking.