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Position Overview 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.   Key Responsibilities   Legacy Data Discovery & Data Model Transformation Participate in structured system inventory efforts to document: Legacy file-based storage structures SAS dataset dependencies Subsystem data flows Manual gating and handoff processes Analyze legacy storage models and design target-state data models aligned to AWS Cloud Native architecture. Replace file-driven batch dependencies with: API-based ingestion Event-driven workflows Database-backed storage (e.g., Aurora/Postgres) Define canonical data schemas and transformation standards.   Cloud-Native Data Architecture Design Architect scalable AWS data pipelines using services such as: S3 Glue Lambda EventBridge SNS/SQS Aurora/Postgres Batch Athena Design data ingestion, staging, transformation, and validation workflows. Establish schema management, versioning, and data lineage practices. Optimize data storage for performance, scalability, and cost efficiency. Support serverless and containerized data processing architectures.   Expert Python-Based Data Engineering Develop advanced Python-based data transformation and validation pipelines. Implement modular, reusable data processing components. Optimize large-scale data manipulation for distributed execution. Develop high-performance ETL/ELT frameworks. Embed automated validation checks directly into data pipelines. Expert-level Python proficiency is required , particularly for: High-volume data processing Data validation logic Modular data engineering frameworks   Data Accuracy, Validation & Visibility Design and implement automated data validation frameworks to ensure: Functional equivalence during migration Record-level and aggregate-level consistency Downstream compatibility across subsystems Develop dashboards and reporting mechanisms providing: Data accuracy metrics Pipeline health indicators Variance detection summaries Enable transparency into data transformation impacts across modernization phases. Support regression validation through golden datasets and automated comparisons.   System-of-Systems Data Coordination Coordinate with Senior Developers and Requirements Engineers to align data models with application modernization. Ensure upstream/downstream data contract stability. Prevent data thrashing during phased migration. Support orchestration of gated workflows through automated triggers rather than manual file exchanges. Collaborate across workstreams to establish shared data standards.   DevSecOps & Governance Alignment Integrate data pipelines into CI/CD frameworks. Support infrastructure-as-code alignment (Terraform/CloudFormation collaboration). Ensure compliance with security controls (IAM, encryption, key management). Produce documentation supporting: Architecture review boards Interface control documents Data flow diagrams Support ATO-related data validation evidence. Requirements Required Qualifications 8+ years of experience in data engineering or data architecture. Expert-level proficiency i