Data Engineer - ML/AI Data Platform (Remote)

Job Description

Job Description

At FEI Systems, we create innovative technology solutions to improve the delivery of health and human services because we know when cumbersome administrative processes stand in the way, those who need it most are often left without access to proper care and support. From comprehensive case management software to disaster recovery services and content management information systems used in delivering foreign aid, our solutions are improving the lives of millions of people. We’re looking for a data engineer who shares our commitment to leveraging technology to make a real impact in the world – a professional who knows, beyond all else, that the quality of our products and services is only as good as the company we keep.

All candidates will be required to complete at least one in-person interview as part of our hiring process.

Role Overview

We are seeking a Data Engineer to support Machine Learning and AI initiatives. Working closely with the Solution Architect, Data Architect, DevOps, and Application Engineering teams, this role is responsible for ensuring that data within our cloud-based platform is high quality, well-governed, feature-ready, and production-grade to support model training, deployment, and ongoing operations.

The ideal candidate has 5+ years of cloud data engineering experience with strong proficiency in Snowflake, Python, and SQL, and solid familiarity with AWS-native data services.

Candidates are not expected to arrive with expertise across every area listed. We are looking for demonstrated strength in the core data engineering and Snowflake skills, combined with the initiative and aptitude to grow into the broader scope of the role.

Day-One Priorities & Scope

Immediate focus is Snowflake-based data engineering, pipeline development, and data quality. Feature engineering, model training support, and MLOps contributions are growth areas that will ramp over time as you become embedded with the team.

Key Responsibilities

Data Pipeline Engineering

• 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.

Snowflake & Cloud Data Engineering

• 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.

Feature Engineering & Data Preparation

• 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.

Model Training & MLOps Support

• 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.

Required Skills & Experience

5+ years of hands-on data engineering experience in a cloud environment.

Core Technologies

• Python — strong proficiency for data processing and pipeline development.
• SQL — advanced skills with hands-on Snowflake transformation experience.
• Snowflake — ELT pipeline design, schema optimization, performance tuning, cost management.
• PostgreSQL — experience with querying, data modeling, and analytics; familiarity with SQL Server to PostgreSQL migration a plus.
• AWS — S3, Glue, Athena, Snowflake integration, and managed relational databases (e.g., Aurora, RDS).
• Apache Iceberg / S3 Tables — familiarity with open table format ecosystems.
• Streaming ingestion tools (e.g., Kinesis, Kafka, or equivalent).
• Workflow orchestration tools (e.g., Airflow, Step Functions, or equivalent).

Pipeline & Data Engineering

• Experience with full loads, incremental loads, append-only pipelines, change-based processing, and SCDs.
• Data validation, reconciliation, error handling, and restart/recovery patterns.
• Data modeling for analytics, ML/AI, and downstream application use cases.
• Ability to evaluate pipeline design trade-offs across performance, cost, reliability, and maintainability.

DevOps & Engineering Practices

• Structured SDLC experience with CI/CD pipelines for data and ML workflows.
• API-based and event-driven data integration patterns.
• Distributed data processing environments.

ML/AI Data Foundations

• 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 — including awareness of data security and privacy considerations when working with LLMs.

Education

Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related technical field. Equivalent professional experience will be considered.

Location : Remote

Status : Full time position with full company benefits.

NOTICE: EO/AA/VEVRAA/Disabled Employer – Federal Contractor. FEI Systems participates in E-Verify, a federal program that enables employers to verify the identity and employment eligibility of all persons hired to work in the United States by providing the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS), with information from each new employee’s Form I-9 to confirm work authorization. For more information on E-Verify, please contact DHS at View phone number on us.fitly.work.

Applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, marital status, political affiliation, disability, or genetic information, except where it relates to a bona fide occupational qualification or requirement. FEI Systems creates an Affirmative Action Plan on an annual basis. Pursuant to federal law, the portions of FEI Systems’ Affirmative Action Program that relate to Section 503 (Persons with Disabilities) and/or Section 4212 (Protected Veterans), are available for inspection upon request by applicants and employees during FEI Systems’ normal business hours.