Senior Data Engineer, MLOps [Remote-US]

Quanata is on a mission to help ensure a better world through context-based insurance solutions. They are seeking a Senior Data Engineer with a specialty in MLOps Engineering to drive model development and delivery best practices, operationalize data science solutions, and implement automation across the machine learning lifecycle.

Responsibilities:

• Operationalize key data science solutions that enable risk‑prediction products across underwriting, pricing, claims routing, and marketing
• Design and build ML pipelines using industry best practices, primarily leveraging AWS services like SageMaker, and integrating with tools such as MLflow for experiment tracking and data platforms like Snowflake
• Stand‑up and operate a shared feature store (Snowflake Snowpark + Kafka) that supports both batch and real‑time feature retrieval
• Own real‑time inference services, exposing low‑latency endpoints (SageMaker endpoints or EKS micro‑services) and managing blue/green or canary deployments
• Implement comprehensive testing strategies (including Unit, integration, data validation, model validation, and performance testing) within robust CI/CD pipelines to maintain high platform quality
• Enable ML Governance: Manage ML models and data versioning, experiment tracking, and reproducibility
• Implement event‑driven orchestration that triggers automated retraining, evaluation, and redeployment based on data drift or business events
• Monitor production models for performance, drift, and data quality—and drive automated remediation

Requirements:

• Bachelor degree or equivalent relevant experience
• 8 years of industry experience with 2 years focused in MLOps and 2 years in software engineering or equivalent experience
• Comprehensive experience in Python and docker
• Familiarity with build tooling such as bash and bazel
• Advanced proficiency in IaC principles and tools like Terraform
• Demonstrated expertise in designing, deploying, and managing scalable and resilient MLOps solutions on AWS
• Applied expertise in the end-to-end machine learning lifecycle, including data ingestion, preprocessing, model training, deployment, and production monitoring
• Excellent written and verbal communication with a strong collaborative focus
• Proficiency in designing and implementing workflows using tools like AWS Step Functions
• Experience with CI/CD tailored for machine learning systems (e.g., automating model training, validation, and deployment)
• Experience in designing and developing large-scale distributed systems, complex APIs, or contributing significantly to platform-level software engineering projects
• Proficiency in utilizing Snowflake's advanced capabilities for ML, such as Snowpark for Python/Java/Scala development, creating and managing user-defined functions (UDFs) for in-database scoring, or integrating directly with external model training and serving platforms
• Prior experience working within the insurance industry or another highly regulated environment, demonstrating an understanding of pertinent regulatory, security, and data governance challenges