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Senior Data Engineer Real-Time ML Infrastructure- 1469

Senior Data Engineer Real-Time ML Infrastructure- 1469

AkubeSeattle, Washington, United States
30+ days ago
Job type
  • Full-time
Job description

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
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