Role Summary Builds high-performance, reliable data systems, including real-time pipelines, tracking, and Feature Store foundations — enabling both analytics and ML production.
Minimum Requirements (Must Have):
— Strong software engineering fundamentals (clean code, testing, system design) — Proven experience building real-time, low-latency data services — Experience operating in production at scale (high throughput, distributed systems) — Strong background with modern data stack (e.g., Kafka/Kinesis, Spark/Flink/Beam, Snowflake/BigQuery/Redshift) — Ability to design schemas, ingestion flows, and data quality frameworks — Experience collaborating with ML teams on feature availability and consistency
Preferred Qualifications (Nice to Have):
— Experience supporting ML systems (feature serving, online/offline consistency) — Production experience with model inference pipelines — Strong DevOps experience (Docker, Kubernetes, CI/CD) — Experience with cloud-native architectures (AWS/GCP) — Experience implementing data observability / lineage frameworks
