Description

IMDb is the world’s most popular and authoritative source for information on movies, TV shows, and celebrities. With more than 250 million monthly unique visitors, IMDb connects entertainment fans worldwide with comprehensive information on over 12 million titles and 13 million cast and crew members.

We’re seeking a Data Engineer who is both a fan of entertainment and understands the business of film and television. You’ll help modernize our data infrastructure by upgrading legacy systems with scalable, cloud-native solutions - while leveraging AI to automate and enhance data pipelines. This role requires technical depth, industry knowledge, and a willingness to experiment with emerging technologies.

Key job responsibilities
• Design, build, and maintain batch and streaming data pipelines processing terabytes of entertainment metadata, user traffic and engagement metrics, and entertainment industry data
• Build ETL CDK pipelines using AWS services including S3, Glue, EMR, Redshift, Kinesis, and MWAA (Airflow)
• Implement AI-assisted data engineering workflows including LLM-powered data quality checks, automated schema evolution, and intelligent data cataloging
• Collaborate with Data Scientists to productionize GenAI and ML models that support content moderation, user personalization, customer segmentation, and more
• Partner with Business Intelligence Engineers to build data models supporting analytics, reporting, and business decision-making
• Optimize query performance and data storage costs across petabyte-scale datasets
• Establish data quality frameworks and monitoring systems to ensure accuracy of entertainment industry data
• Mentor junior engineers on data engineering best practices and AWS technologies


About the team
You’ll work alongside Data Engineers who are building the data foundation for IMDb’s reporting and Insight products; Data Scientists that are building prediction and customer segmentation models; and Business Intelligence Engineers that deliver insights to IMDb stakeholders. Our team values technical excellence, experimentation, and using data to improve the entertainment discovery experience for millions of users.