Senior Data Engineer - MLOps Engineering

About Us

Quanata is on a mission to help ensure a better world through context-based insurance solutions. We are an exceptional, customer-centered team with a passion for creating innovative technologies, digital products, and brands. We blend some of the best Silicon Valley talent and cutting-edge thinking with the long-term backing of leading insurer, State Farm.

Our Team

From data scientists and actuaries to engineers, designers, and marketers, we’re a world-class team of tech-minded professionals from some of the best companies in Silicon Valley and around the world. We’ve come together to create the context-based insurance solutions and experiences of the future. We know that the key to our success isn't just about nailing the technology—it’s hiring the talented people who will help us continue to make a quantifiable impact.

About The Role

We’re seeking a highly skilled Senior Data Engineer with expertise in MLOps Engineering to join our dynamic team. In this role, you will be instrumental in driving the development and deployment of scalable, automated machine learning solutions. Your primary focus will be on operationalizing data science models, designing robust ML pipelines, and ensuring seamless integration across various platforms. You will collaborate closely with data scientists and data engineers to build a resilient platform that accelerates the time-to-market for new models, supporting risk prediction products across underwriting, pricing, claims routing, and marketing functions.

This position involves designing and implementing industry-best practices for ML workflows, leveraging AWS services such as SageMaker, and integrating tools like MLflow for experiment tracking. You will establish and manage shared feature stores, support real-time inference services, and oversee deployment strategies including blue/green and canary releases. Additionally, you will implement comprehensive testing within CI/CD pipelines, manage ML model governance, and automate retraining processes triggered by data drift or business events. Monitoring production models for performance, data quality, and drift, along with driving automated remediation, will be key responsibilities to ensure high-quality, reliable ML solutions.

Qualifications

• Bachelor’s degree or equivalent relevant experience
• Minimum of 8 years of industry experience in data engineering, with at least 2 years focused on MLOps
• At least 2 years of experience in software engineering or related fields
• Proficiency in Python and Docker
• Experience with build tools such as Bash and Bazel
• Advanced knowledge of Infrastructure as Code (IaC) tools like Terraform
• Hands-on experience deploying and managing scalable MLOps solutions on AWS, including SageMaker
• Deep understanding of the end-to-end machine learning lifecycle, including data ingestion, preprocessing, training, deployment, and monitoring
• Strong communication skills and collaborative mindset
• Experience with AWS Step Functions for workflow orchestration
• Proficiency in CI/CD pipelines tailored to ML systems
• Experience with Snowflake, including Snowpark for Python/Java/Scala, user-defined functions, and in-database scoring
• Prior experience in the insurance industry or highly regulated environments, with knowledge of data governance and security protocols
  
  

Responsibilities

• Operationalize key data science solutions supporting risk prediction across various insurance domains
• Design, develop, and maintain scalable ML pipelines using industry standards and AWS services
• Establish and manage shared feature stores supporting both batch and real-time data retrieval
• Own and optimize real-time inference services, managing low-latency endpoints and deployment strategies
• Implement comprehensive testing strategies within CI/CD pipelines to ensure platform robustness
• Manage ML model and data versioning, experiment tracking, and reproducibility to enforce ML governance
• Develop event-driven orchestration workflows for automated retraining, evaluation, and redeployment based on data drift or business triggers
• Monitor production models for performance, accuracy, and data quality, implementing automated remediation as needed
• Collaborate with cross-functional teams to enhance platform capabilities and ensure best practices in ML engineering
  
  

Benefits

• Competitive salary range of $213,000 to $300,000, commensurate with experience and skills
• Comprehensive health, dental, vision, and life insurance plans
• Supplemental income plans for you and your dependents
• Access to Headspace app subscription and monthly wellness allowance
• 401(k) plan with company match to support your financial future
• One-time $2,000 equipment allowance for home office setup
• Fully provisioned MacBook Pro for remote work
• Four weeks of paid time off in your first year, plus twelve weeks of fully paid parental leave
• Support for personal and professional development with up to $5,000 annually for education and training, LinkedIn Learning access, and coaching opportunities through BetterUp
• Remote-first work environment allowing flexibility to work from anywhere in the U.S. (excluding territories)
• Core collaboration hours from 9AM to 2PM Pacific Time
  
  

Equal Opportunity

Quanata, LLC is an equal opportunity employer. We are proud to be an Equal Employment Opportunity employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or any other basis protected by federal, state, or local law.