Data Engineer - ML/AI Data Platform (Remote)

FEI Systems is dedicated to creating innovative technology solutions that enhance the delivery of health and human services. They are seeking a Data Engineer to support Machine Learning and AI initiatives, focusing on maintaining high-quality data within their cloud-based platform to support model training and deployment.

Responsibilities:

• Design, build, and maintain scalable data pipelines supporting ML/AI workloads
• Engineer pipeline patterns including full loads, incremental loads, change-based loads, and slowly changing dimensions
• Ensure pipelines are reliable, performant, secure, and maintainable, troubleshoot and monitor pipelines within an AWS ecosystem
• Perform data transformations in Snowflake using SQL and native Snowflake features
• Design and optimize schemas, tables, views, and materialized views for ML/AI consumption
• Support AWS-native data lake patterns using S3, Glue, Athena, Apache Iceberg, and S3 Tables
• Perform data cleansing, normalization, and enrichment to support ML model development
• Design and implement feature engineering pipelines including aggregation and transformation
• Ensure consistency, reuse, and versioning of features across models and use cases
• Support feature store patterns to enable feature discoverability and reuse
• Collaborate with ML engineers and data scientists to operationalize features into training pipelines
• Support model training workflows, including dataset preparation and scheduled refreshes
• Ensure training datasets and features are reproducible, traceable, and auditable
• Integrate data pipelines into CI/CD workflows; support version control, testing, and deployment of data assets
• Monitor pipeline health, data freshness, and downstream impact on ML/AI systems

Requirements:

• 5+ years of hands-on data engineering experience in a cloud environment
• Strong proficiency in Python for data processing and pipeline development
• Advanced skills in SQL with hands-on Snowflake transformation experience
• Experience with ELT pipeline design, schema optimization, performance tuning, and cost management in Snowflake
• Experience with querying, data modeling, and analytics in PostgreSQL; familiarity with SQL Server to PostgreSQL migration is a plus
• Familiarity with AWS services including S3, Glue, Athena, and managed relational databases (e.g., Aurora, RDS)
• Familiarity with Apache Iceberg / S3 Tables and open table format ecosystems
• Experience with streaming ingestion tools (e.g., Kinesis, Kafka, or equivalent)
• Experience with workflow orchestration tools (e.g., Airflow, Step Functions, or equivalent)
• Experience with full loads, incremental loads, append-only pipelines, change-based processing, and slowly changing dimensions (SCDs)
• Experience with data validation, reconciliation, error handling, and restart/recovery patterns
• Experience with data modeling for analytics, ML/AI, and downstream application use cases
• Ability to evaluate pipeline design trade-offs across performance, cost, reliability, and maintainability
• Structured SDLC experience with CI/CD pipelines for data and ML workflows
• Experience with API-based and event-driven data integration patterns
• Experience in distributed data processing environments
• Understanding of data requirements for ML/AI workloads
• Experience preparing training datasets and features from enterprise data lakes
• Familiarity with reproducibility, dataset versioning, and data lineage concepts
• Familiarity with GenAI concepts relevant to data engineering, such as embedding pipelines, vector databases, retrieval-augmented generation (RAG) data flows, or prompt-driven data processing
• Awareness of data security and privacy considerations when working with LLMs
• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related technical field. Equivalent professional experience will be considered