Senior Data Engineer, MLOps [Remote-US]

## About the role 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. ## Your day-to-day - 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. ## About you - Bachelor degree or equivalent relevant experience and; - 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) ## Bonus points - 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.