September 14, 2021

  • 3+ years of experience as a Data Engineer or in a similar role
  • Experience with data modeling, data warehousing, and building ETL pipelines
  • Experience in SQL
The IMDb TV team is shaping the future of digital video entertainment. Our mission is to build earth’s most customer centric ad supported premium video service and make it trivially easy for hundreds of millions of customers to enjoy. In order to achieve that, we need to build a solid data platform that allows business to learn more about our customers and provide a holistic view of the customers’ journey through IMDb TV.
We are looking for a talented Data Engineer to build up our data platform and put together the Ads, content, and customer engagement data into one scalable design. As a Data Engineer, You are passionate about building long term sustainable architecture.
Key Performance Areas:
  • Design, implement and support an analytical data infrastructure
  • Managing AWS resources including EC2, EMR, S3, Glue, Redshift, etc.
  • 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
  • Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency
  • Collaborate with Data Scientists and Business Intelligence Engineers (BIEs) to recognize and help adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation
  • Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers
  • Experience working with AWS big data technologies (Redshift, S3, EMR)
  • Proven success in communicating with users, other technical teams, and senior management to collect requirements, describe data modeling decisions and data engineering strategy
  • Experience providing technical leadership and mentoring other engineers for best practices on data engineering
  • Familiarity with statistical models and data mining algorithms
  • Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
  • Masters in computer science, mathematics, statistics, economics, or other quantitative fields.
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