Senior / Staff Software Engineer, Data Infrastructure

You will design, build, and operate data infrastructure for personalized recommendations, music discovery, analytics, experimentation, and machine learning. You will own streaming and batch systems end to end, create feature-serving infrastructure, improve pipeline reliability and observability, optimize AWS and Snowflake workloads, and guide technical architecture across teams.

Responsibilities

• Build and scale data infrastructure for personalized recommendations and music discovery
• Design streaming pipelines that convert interactions and content signals into recommendation features
• Develop infrastructure for offline model training and online feature serving
• Enable recommendation experimentation through impression and interaction logging
• Own the architecture and reliability of streaming and batch infrastructure
• Build and maintain modular, testable, observable workflows using Dagster
• Define event schemas, data contracts, and ingestion patterns with backend and product engineers
• Improve pipeline reliability and observability through monitoring, alerting, lineage tracking, and automated recovery
• Optimize pipelines and storage for performance, cost efficiency, and scalability across AWS and Snowflake
• Implement infrastructure as code to manage AWS and Snowflake resources
• Define data platform standards for schema evolution, data quality, and pipeline reliability
• Lead technical design discussions and shape data-platform architecture
• Mentor engineers and improve engineering quality, reliability, and maintainability

Requirements

• 7+ years of relevant software engineering, data infrastructure, or distributed-systems experience
• Strong Python programming skills
• Experience building and operating production-grade distributed systems
• Experience building and operating real-time streaming and batch-processing systems
• Experience with data or serving infrastructure for recommendations, personalization, search, or similar ML-powered products
• Understanding of feature computation, online/offline data consistency, and production ML latency and freshness requirements
• Experience with streaming and cloud data infrastructure such as Flink, Kinesis, or Kafka
• Experience designing reliable, observable systems with monitoring, logging, alerting, and recovery
• Understanding of data modeling, schema evolution, and data quality
• Ability to collaborate with ML, data, and product engineering teams and make pragmatic technical decisions
• Track record of delivering scalable infrastructure and improving engineering practices
• Eligibility to work in the US
• Ability to work from the Cambridge or New York office five days per week

Benefits

• Equity package
• 401(k) with 3% employer match and Roth 401(k)
• Medical, dental, and vision insurance with PPO, HSA, and FSA options
• 11 paid holidays
• Unlimited PTO and sick time
• 16 weeks of paid parental leave
• Creative education stipend
• Commuter allowance
• In-office lunch five days per week