Senior Data Engineer

Senior Data Engineer – ML Platform

Location

Remote (EST Hours Preferred)

Job Summary

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.