Job
Description
We are looking for a Data Engineer who will design, implement and support an analytical data infrastructure providing ad-hoc access to large datasets and computing power. Design, implement and support an analytical data infrastructure providing ad-hoc access to large datasets and computing power. Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL and AWS big data technologies. Creation and support of real-time data pipelines built on AWS technologies including Glue, Redshift/Spectrum, Kinesis, EMR and Athena. Continual research of the latest big data and visualization technologies to provide new capabilities and increase efficiency. Working closely with team members to drive real-time model implementations for monitoring and alerting of risk systems. Collaborate with other tech teams to implement advanced analytics algorithms that exploit our rich datasets for statistical analysis, prediction, clustering and machine learning. Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers. ","jobQualifications":" 3-5 + years of industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasets. bachelors degree in Computer Science, Engineering, Mathematics, or a related technical discipline. Demonstrated strength in data modeling, ETL development, and data warehousing. Experience using big data processing technology using Spark. Knowledge of data management fundamentals and data storage principles. Experience using business intelligence reporting tools (Tableau, Business Objects, Cognos, Power BI etc). Experience working with AWS big data technologies (Redshift, S3, EMR, Spark). Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets. Experience working with distributed systems as it pertains to data storage and computing. Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
