Member of Technical Staff, Runway

• 4+ years of industry experience in data engineering
• Strong knowledge of Python
• Experience with data quality, deduplication, and cleaning at scale
• Comfortable working with cloud storage (S3) and managing large datasets
• Experience building and maintaining ETL/CDC pipelines at scale
• Strong SQL skills and experience with multiple database systems (Postgres, columnar databases like ClickHouse/Redshift)
• Humility and open mindedness; at Runway we love to learn from one another

Desirable

• We're looking for a Data Engineer to build and scale the data infrastructure that powers Runway's AI research and business intelligence
• You'll own critical data pipelines spanning production databases, analytics warehouses, and large-scale ML training datasets
• This role sits at the intersection of data engineering, ML infrastructure, and analytics—you'll enable both world-class research and data-driven business decisions
• You'll work on challenging problems at scale: managing billions of rows of multimodal training data, building CDC streams from production systems, optimizing vector databases for ML workflows, and creating the foundational data layer that the entire company relies on
• Our API endpoints for real-time collaboration and media asset management is written in TypeScript, and runs in ECS containers on AWS Fargate
• We leverage multiple AWS-native components, such as S3, CloudFront, Lambda, Kinesis, and SQS, as building blocks of our infrastructure
• Our inference backend is written in Python (PyTorch, TorchScript), and is deployed across multiple clusters / cloud providers
• We use Kubernetes for container orchestration, and k8s-native components such as Flyte, Kueue, and Kyverno efficient job orchestration
• We invest in prometheus and grafana for monitoring, and Terraform to manage our infrastructure
• You'll build and own pipelines for the creation, curation, and processing of large-scale multimodal datasets, including vector database (LanceDB) management and query optimization for ML metadata
• Build and own ETL and CDC streams from Postgres and ClickHouse to analytics warehouses
• Build standardized data transformation layers using dbt to replace ad-hoc SQL queries and create maintainable data models for business analytics
• Manage production databases (Postgres, ClickHouse) and optimize for performance and reliability