Senior Data Engineer

Summary: Working with Data Scientists, Analysts and application teams as a Sr Data Engineer focused on Machine Learning Platform Capabilities. Enabling the teams to develop, deploy, and operate machine learning solutions in production. Key Responsibilities • Lead the design and delivery of Snowflake-based analytical and feature-ready data models to support BI and ML use cases. • Serve as a senior technical owner for core components of the Data Lakehouse and ML Platform, including data ingestion, feature pipelines, metadata, and orchestration. • Architect and implement scalable ML Platform capabilities that support the full ML lifecycle: data preparation, feature engineering, model training, deployment, monitoring, and retraining. • Partner closely with Data Science teams to operationalize machine learning, forecasting, and simulation models, ensuring reproducibility, reliability, and performance in production. • Design and maintain MLOps pipelines and frameworks for model versioning, promotion, rollback, and monitoring across environments. • Establish platform-level CI/CD standards for data and ML workloads, including automated testing, validation, and quality checks. Qualifications Required • Bachelor’s degree in Computer Science or a related technical field, with 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. • Deep 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.