The Data Engineering Lead is responsible for designing and implementing modern, scalable data architectures to support migration of legacy, file-based analytical systems to AWS Cloud Native environments.
This role leads the transformation of legacy SAS-based data storage models—including flat files, batch outputs, and subsystem-specific data artifacts—into structured, governed, and scalable data models optimized for cloud-native processing.
The Data Engineering Lead will ensure data integrity, performance, and visibility across a system-of-systems modernization initiative, while providing technical leadership for data modeling, ingestion patterns, validation frameworks, and transparency reporting.
Expert-level proficiency in Python and strong experience designing AWS-based data architectures are required.
Architect scalable AWS data pipelines using services such as S3, Glue, Lambda, EventBridge, SNS/SQS, Aurora/Postgres, Batch, Athena.
Develop advanced Python-based data transformation and validation pipelines.

8+ years of experience in data engineering or data architecture.
Expert-level proficiency in Python for data engineering.
Demonstrated experience transforming legacy file-based systems into cloud-native data architectures.
Experience developing data models for high-volume, data-intensive applications.
Deep experience with AWS data services (Glue, Lambda, S3, Aurora/Postgres, EventBridge, etc.).
Experience designing scalable ETL/ELT pipelines.
Experience building analytical dashboards (e.g., QuickSight or equivalent).
Experience implementing automated data validation and quality controls.
Experience working in Agile Scrum Teams.
U.S. Citizenship required.
Preferred Qualifications
Experience modernizing SAS-based data environments.
Experience supporting system-of-systems integration programs.
Experience implementing data lineage and metadata management.
Experience operating in regulated or federal environments.

Data Engineering Lead

Key skills