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Senior Data Engineer, MLOps [Remote-US]

12 hours, 25 minutes ago
United States
Full-time
Senior
Data Engineer
Software Development
AWS Bash CI/CD Docker Java Kafka Kubernetes Machine Learning MLflow MLOps Python Scala Snowflake Terraform
Apply Now
Quanata

Quanata

Quanata is a software development company based in San Francisco, specializing in context-based insurance solutions. The company leverages AI, real-time telematics, and data science to enhance risk prediction, promote safer driving behaviors, and create modern insurance products. Quanata aims to transform the insurance industry by fostering positive behaviors and advancing digital experiences. The company develops a range of software platforms and tools for insurers. Their offerings include AI-powered risk assessment, telematics for driver monitoring, and claims solutions that optimize and automate processes. Quanata also focuses on customer engagement through personalized products and retention tools, supporting insurtech modernization with big data analytics and cloud-native platforms. With a team of around 26 professionals, Quanata draws on talent from Silicon Valley to drive innovation in the insurance sector.

information technology & services
201-500
View All Jobs 7

Description

  • Operationalize data science solutions that support underwriting, pricing, claims routing, and marketing products.
  • Design and build machine learning pipelines using AWS services, MLflow, and Snowflake.
  • Stand up and operate a shared feature store supporting both batch and real-time feature retrieval.
  • Own real-time inference services and manage low-latency endpoint deployments on SageMaker or EKS.
  • Implement testing strategies for machine learning systems, including unit, integration, data validation, model validation, and performance tests.
  • Build and maintain CI/CD pipelines for machine learning platform quality and release automation.
  • Manage model and data versioning, experiment tracking, and reproducibility to support ML governance.
  • Implement event-driven orchestration for automated retraining, evaluation, and redeployment.
  • Monitor production models for performance, drift, and data quality, and drive automated remediation.
  • Partner with data engineers and data scientists to improve the model development and delivery platform.

Requirements

  • Bachelor’s degree or equivalent relevant experience.
  • 8 years of industry experience, including 2 years focused on MLOps and 2 years in software engineering, or equivalent experience.
  • Strong experience with Python and Docker.
  • Familiarity with build tools such as Bash and Bazel.
  • Advanced proficiency with infrastructure-as-code principles and tools such as Terraform.
  • Demonstrated experience designing, deploying, and managing scalable, resilient MLOps solutions on AWS.
  • Applied expertise across the end-to-end machine learning lifecycle, including data ingestion, preprocessing, training, deployment, and production monitoring.
  • Excellent written and verbal communication skills with a strong collaborative mindset.
  • Experience designing and implementing workflows with AWS Step Functions.
  • Experience with CI/CD for machine learning systems, including automating model training, validation, and deployment.
  • Bonus points for experience with large-scale distributed systems, complex APIs, or platform-level software engineering.
  • Bonus points for Snowflake advanced ML capabilities such as Snowpark, UDFs for in-database scoring, or integration with external training and serving platforms.
  • Bonus points for experience in insurance or another highly regulated environment.

Benefits

  • Salary range of $213,000 to $300,000.
  • Medical, dental, vision, life insurance, and supplemental income plans for employees and dependents.
  • Headspace app subscription and a monthly wellness allowance.
  • 401(k) plan with company match.
  • One-time $2,000 work-from-home equipment stipend plus a fully provisioned MacBook Pro.
  • Four weeks of PTO in the first year.
  • Twelve weeks of fully paid parental leave for both birthing and non-birthing parents.
  • Up to $5,000 per year for professional learning, continuing education, and career development, plus LinkedIn Learning and BetterUp coaching.
  • Remote-first work environment with the option to work from anywhere in the U.S., excluding U.S. territories.
  • Core collaboration hours from 9 AM to 2 PM Pacific time.

Interested in this position?

Apply directly on the company website

Apply Now

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