Senior Data Engineer, MLOps

We're looking for a Senior Data Engineer with a specialty in MLOps Engineering that can help drive the organization toward model development and delivery best practices. You will help shape and implement automation across the machine learning lifecycle from data collection to model training to model monitoring. In this high impact role, you will partner with both data engineers focused on data science service delivery and data scientists to develop a robust platform that shortens the time to market of new data science models at Quanata. 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.