Senior Data Engineer, MLOps

Summary

Join Quanata, a company on a mission to improve the world through context-based insurance solutions, as a Senior Data Engineer specializing in MLOps. You will play a key role in driving the organization towards model development and delivery best practices, implementing automation across the machine learning lifecycle. Partnering with data engineers and scientists, you will develop a robust platform to shorten the time to market for new data science models. This high-impact role demands expertise in designing, deploying, and managing scalable MLOps solutions on AWS, along with proficiency in Python, Docker, and IaC tools like Terraform. The position offers a competitive salary and a comprehensive benefits package, including health insurance, paid time off, parental leave, and professional development opportunities. Quanata is a remote-first company, providing flexibility in work location.

Requirements

• 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)

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

Preferred Qualifications

• 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

Benefits

• Medical, dental, vision, life insurance and supplemental income plans for you and your dependents
• A Headspace app subscription
• Monthly wellness allowance
• A 401(k) Plan with a company match
• A one-time payment of $2K will be provided to cover the purchase of in-home office equipment and furniture at your discretion
• Our teams work with MacBook Pros, which we will deliver to you fully provisioned prior to your first day
• All employees accrue four weeks of PTO in their first year of employment
• New parents receive twelve weeks of fully paid parental leave which may be taken within one year after the birth and/or adoption of a child
• The twelve weeks is applicable to both birthing and non-birthing parent
• All employees receive up to $5000 each year for professional learning, continuing education and career development
• All team members also receive LinkedIn Learning subscriptions and access to multiple different coaching opportunities through BetterUp