What You’ll Do

• Design, build, and maintain scalable data ingestion pipelines using AWS services such as Kinesis, Firehose, Lambda, S3, and Glue. - Own the architecture and reliability of core data infrastructure, including both streaming and batch pipelines. - Build and maintain data workflows and orchestration using Dagster, ensuring pipelines are modular, testable, and observable. - Partner with backend and product engineers to define event schemas, data contracts, and ingestion patterns from production systems. - Improve the reliability and observability of data pipelines through monitoring, alerting, lineage tracking, and automated recovery mechanisms. - Collaborate with Analytics and ML teams to ensure the data platform supports a wide range of use cases, from reporting to model training. - Optimize pipelines and storage for performance, cost efficiency, and scalability across AWS and Snowflake. - Implement infrastructure as code (e.g., Terraform) to manage AWS and Snowflake resources in a consistent, reproducible way. - Contribute to data platform standards, including schema evolution, data quality enforcement, and pipeline reliability best practices. - Lead technical design discussions and help shape the future architecture of Suno’s data platform. - Mentor other engineers and raise the bar for engineering quality, reliability, and maintainability. # What You’ll Need

• 5-7+ years of experience as a Software Engineer, Data Engineer, or in a similar infrastructure-focused role. - Strong programming skills (Python preferred) with experience building production-grade distributed systems. - Deep experience building and operating data pipelines, including both real-time (streaming) and batch processing systems. - Hands-on experience with AWS data services, including Kinesis (Streams), Firehose, Lambda, and Glue. - Strong experience with Snowflake, including data modeling, performance optimization, and large-scale data processing. - Experience with orchestration frameworks such as Dagster (or similar tools like Airflow), with an emphasis on building maintainable, observable workflows. - Proficiency with dbt for managing transformations, testing, and modular data modeling. - Experience designing systems for reliability and observability (monitoring, logging, alerting, tracing). - Familiarity with infrastructure as code (e.g., Terraform) and managing cloud resources programmatically. - Strong understanding of data warehousing, data modeling, and pipeline design best practices. - Experience collaborating cross-functionally with data, ML, and product engineering teams. - Ability to operate in ambiguous environments and make pragmatic, high-quality technical decisions. - A strong sense of ownership and a track record of delivering scalable infrastructure systems. - A genuine passion for music, creativity, and AI, and excitement about enabling the next wave of generative creation through robust data infrastructure. ## Additional Notes