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Senior Data Engineer, MLOps [Remote-US]

2 months, 1 week ago
United States
Full-time
Senior
Data Engineer
Software Development
AWS Bash CI/CD Docker Java Kafka Kubernetes Microservices MLflow MLOps Python SageMaker Scala Snowflake Terraform
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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 risk-prediction products across underwriting, pricing, claims routing, and marketing.
  • Design and build machine learning pipelines using AWS services, including SageMaker, along with tools such as MLflow and Snowflake.
  • Stand up and operate a shared feature store that supports both batch and real-time feature retrieval.
  • Own real-time inference services and manage low-latency deployment patterns such as blue/green or canary releases.
  • Implement testing strategies for machine learning systems, including unit, integration, data validation, model validation, and performance testing.
  • Build and maintain CI/CD pipelines tailored to machine learning workflows.
  • Manage model and data versioning, experiment tracking, and reproducibility to support ML governance.
  • Implement event-driven orchestration for automated retraining, evaluation, and redeployment based on drift or business triggers.
  • Monitor production models for performance, drift, and data quality, and drive automated remediation.
  • Partner with data engineers and data scientists to improve model delivery speed and platform reliability.

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 tooling such as bash and Bazel.
  • Advanced proficiency in infrastructure as code principles and tools such as Terraform.
  • Demonstrated expertise designing, deploying, and managing scalable MLOps solutions on AWS.
  • Applied experience across the end-to-end machine learning lifecycle, including data ingestion, preprocessing, training, deployment, and production monitoring.
  • Experience designing and implementing workflows with tools such as AWS Step Functions.
  • Experience with CI/CD for machine learning systems, including automated model training, validation, and deployment.
  • Excellent written and verbal communication skills with a strong collaborative focus.
  • Experience designing large-scale distributed systems, complex APIs, or platform-level software engineering projects is preferred.
  • Proficiency with Snowflake advanced ML capabilities, such as Snowpark, UDFs, or integration with external training and serving platforms is preferred.
  • Experience in the insurance industry or another highly regulated environment is preferred.

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 home office equipment and furniture stipend.
  • 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 access.
  • Remote-first work environment within the U.S., with occasional travel possible and optional office access in select locations.
  • Core meeting 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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