data
Posted Dec 10, 2025Senior Data Engineer, MLOps [Remote-US]
at Quanata
Remote
Responsibilities
- 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.
- 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
Requirements
- 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.
- Stand‑up and operate a shared feature store (Snowflake Snowpark + Kafka) that supports both batch and real‑time feature retrieval.
- Bachelor 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.
- proficiency in designing and implementing workflows using tools like AWS Step Functions •
- Experience in designing and developing large-scale distributed systems, complex APIs, or contributing significantly to platform-level software engineering projects.