Lead Data Design and Test Engineer

The MITRE Corporation•New Bedford, MA

11h•Hybrid

About The Position

Why choose between doing meaningful work and having a fulfilling life? At MITRE, you can have both. That's because MITRE people are committed to tackling our nation's toughest challenges—and we're committed to the long-term well-being of our employees. MITRE is different from most technology companies. We are a not-for-profit corporation chartered to work for the public interest, with no commercial conflicts to influence what we do. The R&D centers we operate for the government create lasting impact in fields as diverse as cybersecurity, healthcare, aviation, defense, and enterprise transformation. We're making a difference every day—working for a safer, healthier, and more secure nation and world. Our workplace reflects our values. We offer competitive benefits, exceptional professional development opportunities for career growth, and a culture of innovation that embraces adaptability, collaboration, technical excellence, and people in partnership. If this sounds like the choice you want to make, then choose MITRE - and make a difference with us. Department Summary: We are seeking a Data Design and Validation Engineer to support our AWS Cloud Data Warehouse (CDW) team. This role primarily focuses on designing efficient data models and schemas while also ensuring data correctness and validation to maintain quality and reliability. The ideal candidate will play a key role in building scalable, well-structured data solutions while embedding validation mechanisms to uphold data integrity.

Requirements

Minimum Experience: 8 years in Enterprise Data Warehouse technology, including data modeling, schema design, and testing.
Bachelor’s degree with eight years’ related experience, or a master’s degree with six years’ related preferably with a technical major such as engineering, computer science, etc.
Data Modeling Expertise: Strong experience in designing and managing relational database schemas and access, with SQL and PL/SQL proficiency.
Testing Skills: Experience in ETL/ELT testing, data validation, and data quality assurance, including Python for automation and testing.
AWS Knowledge: Familiarity with AWS CDW services (e.g., Redshift, Glue, EMR) and best practices for cloud data storage and integration.
This position requires a minimum of 60% hybrid on-site.

Nice To Haves

Cloud Data Architecture Knowledge: Familiarity with cloud architecture principles, especially in AWS, with understanding of how to design scalable, cost-efficient, and secure data models.
Performance Optimization: Proven skills in tuning SQL queries, database indexing, and caching mechanisms to optimize data retrieval and improve model performance.
Documentation and Technical Writing: Strong documentation skills for creating clear, user-friendly technical documents, including test case repositories, design specs, and troubleshooting guides.
Project Management Skills: Familiarity with project management tools (e.g., Jira, Asana) and methodologies, enabling effective prioritization, tracking, and reporting on testing and design tasks.
Interpersonal Skills: Proven ability to collaborate effectively and actively participate in cross-training and team knowledge transfer.

Responsibilities

Collaborate with business and technical teams to translate requirements into efficient data models and schemas optimized for AWS CDW.
Design and implement data architecture solutions, ensuring scalability, performance, and security.
Develop data integration frameworks that support seamless ingestion, transformation, and consumption of structured and semi-structured data.
Establish and enforce best practices for schema management, including versioning, indexing, and partitioning strategies.
Work with teams to improve data accessibility and usability, ensuring models are optimized for analytics, reporting, and operational use cases.
Maintain technical documentation, including data dictionaries, schema diagrams, and modeling standards to ensure consistency and clarity.
Define data validation rules that help maintain consistency and accuracy across ingestion and transformation processes.
Develop lightweight automated validation scripts that support early detection of data quality issues while integrating with development workflows.
Write targeted test cases for critical data components, ensuring they can be integrated into CI/CD pipelines for ongoing validation.
Conduct selective data audits and quality checks, ensuring schema changes and transformations do not introduce inconsistencies.
Work closely with engineering teams to ensure data pipelines are designed with built-in validation mechanisms, minimizing rework and downstream issues.