Data Engineer – ML Platform

Pyramid Consulting, Inc. is seeking a talented Data Engineer – ML Platform for a contract opportunity with long-term potential. The role involves building and operating data platforms in AWS, contributing to ML Platform or MLOps frameworks, and ensuring best practices in model lifecycle management and deployment.

Responsibilities:

• Must have skills: - ML Platform, MLOps pipelines, AWS, Python, SQL
• 7+ years of experience building and operating data platforms in AWS
• Demonstrated experience designing or contributing to a shared ML Platform or MLOps framework used by multiple teams
• Expertise in Python and SQL, with a strong track record of production-grade data and ML systems
• Strong hands-on experience with MLOps best practices, including:
• Model lifecycle management and deployment patterns
• Feature engineering and reusable feature pipelines
• Experiment tracking, reproducibility, and model governance
• Model performance monitoring and drift detection
• Advanced knowledge of AWS services including Glue, Lambda, ECS Fargate, and Apache Spark, with experience operating them at scale
• Strong experience delivering containerized, infrastructure-as-code solutions using Docker and CDK
• Deep understanding of data warehousing, lakehouse, and ML-ready data architectures, including Delta Lake; Snowflake experience is a strong plus
• Experience with Arrow-based data and streaming technologies (ADBC, Arrow ODBC, PyArrow) a plus
• Experience with Airflow or Dagster (or similar orchestration platforms) for managing complex data and ML workflows a plus
• Proven ability to influence standards, architecture, and best practices across engineering and data science teams
• Excellent communication skills, with the ability to translate complex platform and ML concepts for technical and non-technical stakeholders

Requirements:

• Must have skills: - ML Platform, MLOps pipelines, AWS, Python, SQL
• 7+ years of experience building and operating data platforms in AWS
• Demonstrated experience designing or contributing to a shared ML Platform or MLOps framework used by multiple teams
• Expertise in Python and SQL, with a strong track record of production-grade data and ML systems
• Strong hands-on experience with MLOps best practices, including: Model lifecycle management and deployment patterns, Feature engineering and reusable feature pipelines, Experiment tracking, reproducibility, and model governance, Model performance monitoring and drift detection
• Advanced knowledge of AWS services including Glue, Lambda, ECS Fargate, and Apache Spark, with experience operating them at scale
• Strong experience delivering containerized, infrastructure-as-code solutions using Docker and CDK
• Deep understanding of data warehousing, lakehouse, and ML-ready data architectures, including Delta Lake; Snowflake experience is a strong plus
• Proven ability to influence standards, architecture, and best practices across engineering and data science teams
• Excellent communication skills, with the ability to translate complex platform and ML concepts for technical and non-technical stakeholders
• Experience with Arrow-based data and streaming technologies (ADBC, Arrow ODBC, PyArrow) a plus
• Experience with Airflow or Dagster (or similar orchestration platforms) for managing complex data and ML workflows a plus