Senior Data Engineer, MLOps [Remote-US]
Quanata · United States
- Hybrid
- Full-time
- $256,500 / year
- United States
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Job highlights
- Build and deploy ML pipelines on AWS.
- Own real-time inference and model monitoring.
- Implement MLOps best practices and governance.
- Collaborate with data scientists and engineers.
- Shape the future of insurance tech.
About the role
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. Learn more about us and our work at quanata.com.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.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
- 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.
About You
- Bachelor's 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.
Key skills/competency
Senior Data Engineer MLOps, Python, Docker, AWS, SageMaker, MLflow, Snowflake, Terraform, CI/CD, Data Pipelines.Skills & topics
- Senior Data Engineer
- MLOps
- Data Engineering
- Machine Learning
- Python
- AWS
- SageMaker
- Snowflake
- CI/CD
- Remote
How to get hired
- Tailor your resume: Highlight MLOps, Python, Docker, AWS, and CI/CD experience.
- Showcase MLOps expertise: Emphasize experience with ML pipelines, model deployment, and monitoring.
- Demonstrate AWS proficiency: Detail your work with SageMaker, Step Functions, and IaC tools.
- Prepare for technical questions: Be ready to discuss ML lifecycle, data platforms, and distributed systems.
- Express collaborative spirit: Highlight your ability to work with cross-functional teams.
Technical preparation
Master Python for data and ML tasks.,Deepen AWS expertise, especially SageMaker.,Practice IaC with Terraform and Docker.,Build CI/CD pipelines for ML models.
Behavioral questions
Describe a challenging MLOps project.,How do you ensure ML model quality?,How do you collaborate with data scientists?,Explain a complex data architecture.
Frequently asked questions
- How does Quanata ensure the safety of applicants during the job search for the Senior Data Engineer MLOps role?
- Quanata prioritizes applicant safety. They will only reach out via email using the domain quanata.com. Any communication not from this domain should be considered a security risk and ignored. This applies to all roles, including the Senior Data Engineer MLOps position.
- What is Quanata's mission and what kind of solutions do they offer?
- Quanata's mission is to help ensure a better world through context-based insurance solutions. They leverage cutting-edge technology and talent to create innovative digital products and brands in the insurance industry.
- What are the primary responsibilities of a Senior Data Engineer MLOps at Quanata?
- The Senior Data Engineer MLOps will shape and implement automation across the machine learning lifecycle, from data collection to model monitoring. This includes designing ML pipelines, operationalizing data science solutions, managing feature stores, owning inference services, and enabling ML governance.
- What are the key technical skills required for the Senior Data Engineer MLOps role?
- Key technical skills include comprehensive experience in Python and Docker, advanced proficiency in IaC principles and tools like Terraform, expertise in MLOps solutions on AWS (SageMaker), and experience with CI/CD for ML systems. Familiarity with build tooling like bash and bazel is also important.
- Does Quanata offer remote work for the Senior Data Engineer MLOps position?
- Yes, this Senior Data Engineer MLOps role is listed as Remote-US. Quanata is a remote-first company for most positions, allowing employees to work from anywhere in the U.S., excluding U.S. territories.
- What is the expected salary range for a Senior Data Engineer MLOps at Quanata?
- The salary range for this position is $213,000 to $300,000 annually. The final salary will be determined by the candidate's skills, experience, and Quanata's internal salary structure.
- What benefits does Quanata offer to its employees?
- Quanata offers a comprehensive benefits package including medical, dental, vision, life insurance, supplemental income plans, a Headspace app subscription, a monthly wellness allowance, and a 401(k) plan with a company match. They also provide a $2K work-from-home equipment stipend and MacBook Pros.