Crunchyroll, LLC
Data Engineer III
About this role
Crunchyroll seeks a Data Engineer III to design and optimize scalable data services and pipelines that support millions of anime fans globally. You'll lead automation initiatives, build event-driven architectures, and collaborate with cross-functional teams to establish best practices and drive continuous improvement across the organization.
What you'll do
- Design, build, and maintain scalable data services and pipelines using TypeScript and Python with modern backend frameworks
- Develop event-driven architectures using AWS services such as SNS/SQS, EventBridge, Kinesis, and Kafka
- Architect and optimize database schemas across relational, NoSQL, and graph databases
- Identify and resolve performance bottlenecks in both frontend and backend systems
- Implement automation for build, deployment, and monitoring processes
- Develop comprehensive test suites and maintain security best practices
What they're looking for
- TypeScript and Node.js
- Python
- AWS cloud architecture and services
- Event-driven systems and messaging (Kafka, SNS/SQS, EventBridge)
- Database design (PostgreSQL, MySQL, DynamoDB, Neo4j)
- Docker and Kubernetes containerization
- CI/CD pipelines
- Microservices and API development
Benefits
- Competitive compensation package with performance bonus potential
- Flexible time off policies
- Opportunity to work on anime content serving 100+ million fans globally
- Collaborative, fast-paced environment with passionate colleagues
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Crunchyroll, LLC
Crunchyroll operates an anime streaming platform serving millions of fans globally. The company is hiring iOS/Apple TV engineers, frontend engineers for monetization and subscription experiences, and senior data engineers to build scalable infrastructure and optimize platform services.
View all jobs at Crunchyroll, LLCLikely interview questions
- Can you walk us through your experience designing and implementing event-driven architectures? Which messaging systems have you used (Kafka, SNS/SQS, EventBridge, Kinesis) and what trade-offs did you consider?
- Tell us about a time you optimized a data pipeline or service for performance. What bottlenecks did you identify and how did you resolve them?