Data Engineer - ML/AI Platform

Salary: $71,000 - 101,000 per year Requirements:

• We need at least 5 years of hands-on data engineering experience in a cloud environment.
• We are looking for strong proficiency in Python for data processing and pipeline development.
• We require advanced SQL skills, including practical Snowflake transformation experience.
• We value deep experience with Snowflake, including ELT design, schema tuning, performance optimization, and cost control.
• We are seeking experience with PostgreSQL for querying, data modeling, and analytics; familiarity with SQL Server to PostgreSQL migration is a plus.
• We want familiarity with AWS services such as S3, Glue, Athena, Snowflake integration, and managed databases like Aurora or RDS.
• We prefer knowledge of Apache Iceberg or S3 Tables and other open table format ecosystems.
• We are looking for experience with streaming ingestion tools such as Kinesis, Kafka, or similar platforms.
• We expect experience with workflow orchestration tools such as Airflow, Step Functions, or equivalent.
• We need experience building full-load, incremental, append-only, change-based, and slowly changing dimension pipelines.
• We require experience with data validation, reconciliation, error handling, and restart/recovery patterns.
• We value structured SDLC experience with CI/CD pipelines for data and ML workflows.
• We seek understanding of data requirements for ML and AI workloads, including training dataset preparation and feature creation.
• We prefer familiarity with reproducibility, dataset versioning, and data lineage concepts.
• We welcome exposure to GenAI-related data engineering topics such as embedding pipelines, vector databases, RAG data flows, and prompt-driven processing, along with awareness of data security and privacy considerations.
• A bachelors degree in Computer Science, Data Engineering, Information Systems, or a related technical field is preferred; equivalent professional experience will be considered.

• We design, build, and maintain scalable data pipelines that support ML and AI workloads.
• We develop pipeline patterns for full loads, incremental loads, change-based loads, and slowly changing dimensions.
• We keep pipelines reliable, performant, secure, and maintainable, while troubleshooting and monitoring them in an AWS environment.
• We transform data in Snowflake using SQL and native Snowflake capabilities.
• We design and optimize schemas, tables, views, and materialized views for ML and AI consumption.
• We support AWS-native data lake patterns using S3, Glue, Athena, Apache Iceberg, and S3 Tables.
• We clean, normalize, and enrich data to support model development.
• We create and implement feature engineering pipelines, including aggregation and transformation.
• We ensure feature consistency, reuse, and versioning across models and use cases.
• We support feature store patterns to improve feature discovery and reuse.
• We collaborate with ML engineers and data scientists to operationalize features in training pipelines.
• We support model training workflows, including dataset preparation and scheduled refreshes.
• We ensure training datasets and features are reproducible, traceable, and auditable.
• We integrate data pipelines into CI/CD workflows and support version control, testing, and deployment of data assets.
• We monitor pipeline health, data freshness, and downstream effects on ML and AI systems.

• AI
• Airflow
• AWS
• CI/CD
• Cloud
• Support
• Kafka
• Model Training
• PostgreSQL
• Python
• RAG
• SQL
• Security
• Snowflake
• API
• Architect
• DevOps
• Embedded
• Machine Learning
• MLOps

More:

We are FEI Systems, and we build innovative technology solutions that improve the delivery of health and human services, helping reduce administrative barriers so people can access the care and support they need. Our work spans case management software, disaster recovery services, and content management systems used in foreign aid delivery, with a mission to create meaningful impact at scale. This is a remote, full-time role with full company benefits. We are committed to equal opportunity and federal contractor compliance, and all candidates will complete at least one in-person interview during our hiring process.

last updated 33 week of 2026