Senior Data Engineer, MLOps [Remote-US]

Role overview

Quanata is seeking a Senior Data Engineer (MLOps specialty) to operationalize and scale machine learning delivery across the ML lifecycle. You’ll build automation from data collection → model training → monitoring, partnering with data engineers and data scientists to reduce time-to-market for new models.

Responsibilities

• Operationalize data science solutions for risk prediction across underwriting, pricing, claims routing, and marketing.
• Design and build ML pipelines using AWS services (primarily SageMaker) and integrate with:
  • MLflow (experiment tracking)
  • Snowflake (data platform)
• Stand up and run a shared feature store using Snowflake Snowpark + Kafka, supporting both batch and real-time feature retrieval.
• Own real-time inference services with low-latency endpoints, such as:
  • SageMaker endpoints
  • EKS micro-services
  • Manage blue/green or canary deployments
• Implement comprehensive testing (unit, integration, data validation, model validation, performance testing) within robust CI/CD pipelines.
• Enable ML governance: manage model and data versioning, experiment tracking, and reproducibility.
• Use event-driven orchestration to automate retraining, evaluation, and redeployment triggered by data drift or business events.
• Monitor production models for performance, drift, and data quality, and drive automated remediation.

Requirements

• Bachelor’s degree or equivalent experience.
• 8+ years industry experience, including:
  • 2 years focused in MLOps
  • 2 years in software engineering
• Strong Python and Docker experience.
• Familiarity with build tooling such as bash and Bazel.
• Advanced knowledge of IaC using Terraform.
• Proven expertise designing, deploying, and managing scalable, resilient MLOps on AWS.
• Applied experience across the end-to-end ML lifecycle (ingestion, preprocessing, training, deployment, production monitoring).
• Strong written and verbal communication and collaboration.
• Experience designing workflows with AWS Step Functions.
• CI/CD experience tailored for ML systems (training, validation, deployment automation).

Nice to have

• Bonus points for additional experience (the posting cuts off after “Bonus points Experience in designin”).