City: Seattle, WA/ Glendale, CA/ Burbank, CA/ Santa Monica, CA Onsite/ Hybrid/ Remote: Onsite (4 days a week onsite) Duration: 19 months Rate Range: Up to$92.50/hr on W2 depending on experience (no C2C or 1099 or sub-contract) Work Authorization: GC, USC, All valid EADs except OPT, CPT, H1B Must Have: • Spark or Flink or Beam or Kafka Streams • Real-time streaming pipeline experience • SQL and large-scale schema design • AWS or equivalent cloud platforms • Python, Java, or Scala • ML workflow support (feature engineering, data validation, etc.) Responsibilities: • Design, build, and maintain scalable batch and real-time data pipelines for user interaction data, metadata, and model features • Develop and operate offline and real-time feature stores to support ML inference and training workflows • Partner with ML engineers to define data schemas, validation logic, and build production-ready datasets • Implement monitoring and observability for data quality and pipeline reliability • Optimize data workflows for speed, cost, and scalability across large-scale datasets • Translate personalization requirements into robust data infrastructure solutions • Participate in selection and adoption of modern data tooling and cloud-native technologies • Contribute to team’s technical excellence through code reviews and design discussions Qualifications: • Bachelor’s or Master’s in Computer Science, Data Engineering, or related technical field • 5+ years of experience building production-grade distributed data systems • Expertise in modern data frameworks (Spark, Flink, Beam, Kafka Streams) • Proficient in SQL and schema design for high-scale data environments • Strong programming skills in Python, Java, or Scala • Experience with cloud platforms such as AWS and infrastructure components like data lakes, warehouses, and feature stores • Proven ability to support machine learning data workflows and pipelines Preferred: • Experience in building ML infrastructure for personalization or recommendation systems • Familiarity with MLOps tools (MLflow, TFX, Kubeflow) • Hands-on knowledge of real-time ML serving and online feature generation • Prior work in early-stage or 0→1 product development environments • Strong cross-functional collaboration with ML and product teams