Senior Data Engineer, MLOps [Remote-US]

Senior Data Engineer – MLOps

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 Silicon Valley talent with the long‑term backing of leading insurer State Farm.

Overview

We’re looking for a Senior Data Engineer with a specialty in MLOps Engineering to 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 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.

Responsibilities

• 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.

Qualifications

• Bachelor's degree or equivalent relevant experience.
• 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 designing and developing large‑scale distributed systems, complex APIs, or contributing 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 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 – $300,000 (determined based on skills, experience, and internal structure).

Benefits

• Comprehensive health, dental, vision, life insurance, and supplemental income plans.
• Headspace app subscription, monthly wellness allowance, and 401(k) plan with company match.
• Remote‑first environment; one‑time $2,000 home‑office equipment reimbursement.
• Paid time off: Four weeks PTO in the first year; twelve weeks paid parental leave.
• Professional development: Up to $5,000 per year for learning, LinkedIn Learning subscriptions, and access to BetterUp coaching.
• Core meeting hours 9 AM – 2 PM Pacific time for collaboration across time zones.

Location

Remote‑first company; work from anywhere in the U.S. Occasional travel may be requested but is not required. Employees based in the San Francisco Bay Area or Providence, Rhode Island may commute to local offices if desired.

Equal Opportunity

Quanata, LLC is an equal opportunity workplace. We are committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. If you are a San Francisco resident, please read the City and County of San Francisco’s Fair Chance Ordinance notice.
If you require a reasonable accommodation, please reach out to your Talent Acquisition Partner for assistance.