Senior Data Engineer, MLOps [Remote-US]

Job Description To help keep everyone safe, we encourage all applicants to pay close attention to protect themselves during their job search. When applying for a position online you are at risk of being targeted by malicious actors looking for personal data. Please be aware we will only reach out via email using the domain . Anything that does not match those domains should be ignored and considered a security risk. 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. 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 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 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. Salary Salary: $213,000 to $300,000* *Please note that the final salary offered will be determined based on the selected candidate/'s skills and experience, as well as the internal salary structure at Quanata. Our aim is to offer a competitive and equitable compensation package that reflects the candidate/'s expertise and contributions to our organization. Additional Details Benefits: We provide a wide variety of health, wellness and other benefits. These include medical, dental, vision, life insurance and supplemental income plans for you and your dependents, a Headspace app subscription, monthly wellness allowance and a 401(k) Plan with a company match. Work from Home Equipment: In a virtual environment, a one-time payment of $2K will be provided to cover the purchase of in‑home office equipment and furniture. We typically provide MacBook Pro laptops fully provisioned prior to your first day. Paid Time Off: All employees accrue four weeks of PTO in their first year. New parents receive twelve weeks of fully paid parental leave which may be taken within one year after birth/adoption. The twelve weeks applies to both birthing and non-birthing parents. Personal and Professional Development: All employees receive up to $5000 each year for professional learning, continuing education and career development. LinkedIn Learning subscriptions and BetterUp coaching are also available. Location: This is a remote-first role for most positions; you may work from anywhere in the U.S., excluding U.S. territories. Occasional travel may be requested or encouraged but is not required. Some positions may require travel per the job description. Employees in SF Bay Area or Providence, RI may commute to local offices if desired. Hours: Core meeting hours are 9:00 AM – 2:00 PM Pacific Time for collaboration across time zones. Quanata, LLC is an equal opportunity workplace. We are committed to equal employment opportunities regardless of race, color, religion, sex, national origin, age, disability, 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 require a reasonable accommodation, please reach out to your Talent Acquisition Partner for assistance. #J-18808-Ljbffr

Created: 2025-09-17