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

1. Operationalize key data science solutions that enable risk‑prediction products across underwriting, pricing, claims routing, and marketing. 
2. 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.
3. Stand‑up and operate a shared feature store (Snowflake Snowpark + Kafka) that supports both batch and real‑time feature retrieval.
4. Own real‑time inference services, exposing low‑latency endpoints (SageMaker endpoints or EKS micro‑services) and managing blue/green or canary deployments.
5. Implement comprehensive testing strategies (including Unit, integration, data validation, model validation, and performance testing) within robust CI/CD pipelines to maintain high platform quality. 
6. Enable ML Governance: Manage ML models and data versioning, experiment tracking, and reproducibility.. 
7. Implement event‑driven orchestration that triggers automated retraining, evaluation, and redeployment based on data drift or business events.
8. 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.

Salary: $213,000 to $300,000*

*Please note that the final salary offered will be determined based on the selected candidate's skills, and experience, as well as the internal salary structure at Quanata. Our aim is to offer a competitive and equitable compensation package that reflects the candidate's expertise and contributions to our organization.

Additional Details: 

• Benefits: We provide a wide variety of health, wellness and other benefits.These include medical, dental, vision, life insurance and supplemental income plans for you and your dependents, a Headspace app subscription, monthly wellness allowance and a 401(k) Plan with a company match.
• Work from Home Equipment: Given our virtual environment— in order to set you up for success at home, a one-time payment of $2K will be provided to cover the purchase of in-home office equipment and furniture at your discretion. Also, our teams work with MacBook Pros, which we will deliver to you fully provisioned prior to your first day.
• Paid Time Off: 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.
• Personal and Professional Development: We’re committed to investing in and helping our people grow personally and professionally.  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.
• Location: We are a remote-first company for most positions so you may work from anywhere you like in the U.S, excluding U.S. territories. For most positions, occasional travel may be requested or encouraged but is not required. Some positions might require travel per the job description provided to the employee. Employees based in the San Francisco Bay Area or in Providence, Rhode Island may commute to one of our local offices as desired.  
• Hours: We maintain core meeting hours from 9AM - 2PM Pacific time for collaborating with team members across all time zones.