Sr. Systems Engineer (AWS Data Engineer)

SAIC is searching for a motivated, career and customer-oriented Sr.

AWS Data Engineer who will lead the next phase of our healthcare data platform modernization.

This role is central to our evolution from an MSSQL XML object store toward a scalable, cloud-native AWS data architecture — one that enables business stakeholders and analysts to run performant, SQL-based reports and queries against our healthcare data seamlessly.

This person will help evaluate and decide the right AWS data strategy for our platform.

This is a 100% Remote role.

• Evaluate and recommend the optimal AWS-native data storage and query architecture for our XML object store data — assessing options such as S3 + Athena, Aurora, DynamoDB, Redshift, and OpenSearch based on query patterns, reporting needs, cost, and scalability
• Design and build the reporting and query layer that allows business stakeholders and non-technical users to run relational SQL queries against healthcare data using familiar tooling
• Architect the migration path for XML object store data from MSSQL into the target AWS platform, defining storage formats, partitioning strategies, and data organization for optimal query performance
• Build and maintain AWS data catalog, schemas, and metadata management so that object store data is discoverable and queryable by business users
• Optimize query performance and cost across chosen AWS services — partitioning, compression, file formats, and query pattern tuning
• Work directly with business stakeholders and analysts to understand reporting requirements and translate them into durable, performant data models and query patterns
• Collaborate with backend C#/.NET engineers to ensure data flows cleanly from EDI processing pipelines into the new data layer
• Implement and maintain CI/CD pipelines for data infrastructure using AWS-native tooling (CodePipeline, CodeBuild, CodeDeploy)
• Apply infrastructure as code (IaC) practices using AWS CDK, CloudFormation, or Terraform
• Document architecture decisions, data flows, and data dictionaries clearly for both technical and non-technical audiences