vms@cordova.solutions

Data Engineer

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Demonstrated expertise in data engineering by building predictive modeling datasets and migrating analytical data products

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Proficient in automating and maintaining a large feature store, optimizing scoring processes for machine learning models, and using tools like DataRobot, Netezza, Python, Git, and Airflow to enhance efficiency in advanced analytics

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Skilled in developing enterprise-level analytical weblogs (Clickstream) databases

About Data Engineer

With 15 years of experience in data engineering, this professional possesses a robust background in crafting and maintaining data structures and environments tailored for machine learning and advanced analytics. Collaborating closely with data scientists, they have actively contributed to developing production systems and large data models. Their expertise extends to creating custom Python modules for internal database operations, variable transformation, and predictive model implementation, resulting in successfully deploying over 30 production predictive models for clients.

With proficiency in SQL, they have designed more than 500 data tables across diverse technology stacks. Furthermore, as a project lead, they excel in communication, bridging gaps between leadership, software engineers, and data scientists. Their innovative spirit is evident through their involvement in cutting-edge proof of concepts, including technologies like DataRobot, and their ability to integrate new solutions into complex, regulated environments. Currently, they play a pivotal role in supporting model and system migrations during one of the most significant corporate integrations in the financial sector’s history.

Skills

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SQL

Designed and managed 500+ data tables across various technology stacks, facilitating reporting, analytics, and data science. Accomplishments also include obtaining a patent for pioneering data architecture and engineering on a high-capacity web analytics data pipeline processing 400 million daily records.
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Python

10+ years of leveraging Python for automation, data science development, production processes, and seamless tool integration