Senior Data Engineer

Senior Data Engineer ML Platform Location Job Summary Remote (EST Hours Preferred) 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. The selected candidate will play a key role in designing, building, and optimizing enterprise-grade data lakehouse and ML platform solutions that support advanced analytics, machine learning, and AI-driven applications across the organization. Key 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. Required Qualifications • 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. Preferred Qualifications • 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.