Senior Data Engineer

STAFFXPERT LLC is seeking a Senior Data Engineer ML Platform on behalf of our client in a remote capacity supporting EST hours. This role is ideal for an experienced data engineering professional with strong expertise in cloud-native data platforms, MLOps, and scalable machine learning infrastructure, focusing on designing, building, and optimizing enterprise-grade data lakehouse and ML platform solutions.

Responsibilities:

• Design, develop, and maintain scalable data lakehouse and machine learning platform components
• Build and optimize data ingestion pipelines, feature engineering workflows, and orchestration frameworks
• Partner with Data Science teams to operationalize machine learning models into production environments
• Develop and manage MLOps pipelines for model deployment, monitoring, versioning, and retraining
• Implement CI/CD best practices for data and ML workloads, including automated testing and validation
• Design and support analytical and feature-ready data models using modern cloud data architectures
• Establish governance standards for data, features, and models, including lineage, metadata, security, and auditability
• Implement monitoring solutions for data quality, model performance, and drift detection
• Build and support containerized services using Docker and cloud-native infrastructure tools
• Collaborate cross-functionally with engineering and product teams to expose data and ML capabilities through APIs and services
• Provide technical leadership and mentor junior engineers on platform and architecture best practices

Requirements:

• Bachelor s degree in Computer Science, Engineering, or a related technical field
• 7+ years of experience designing and supporting enterprise-scale data platforms in AWS environments
• Strong expertise in Python and SQL with experience building production-grade data systems
• Hands-on experience with AWS services such as Glue, Lambda, ECS Fargate, and Apache Spark
• Proven experience working with MLOps frameworks and machine learning lifecycle management
• Strong understanding of: Model deployment and monitoring, Feature engineering pipelines, Experiment tracking and reproducibility, Model governance and drift detection
• Experience building containerized and Infrastructure-as-Code solutions using Docker and CDK
• Deep understanding of modern data warehousing, lakehouse, and ML-ready data architectures
• Excellent communication and collaboration skills with the ability to work across technical and business teams
• Experience with Snowflake and Delta Lake architectures
• Familiarity with orchestration tools such as Airflow or Dagster
• Experience with Arrow-based technologies including PyArrow, Arrow ODBC, or ADBC
• Prior experience influencing enterprise data architecture standards and engineering best practices