Design and build scalable data platform capabilities that empower domain teams through extensible frameworks and standards, enabling them to manage their data assets within a cohesive semantic architecture spanning the enterprise
Implement robust data integration solutions and unified namespace patterns that create a digital thread connecting real-time and historical information across the entire product lifecycle—from design through manufacturing to field operations
Build optimized data storage architectures and metadata management systems including data lakes, warehouses, knowledge graphs, and enterprise catalogs that make all organizational knowledge discoverable and actionable
Develop semantic layers and ontologies that capture domain expertise and business logic, making implicit organizational knowledge explicit and machine-readable in formats that both humans and AI systems can leverage, while preserving traceability across the product lifecycle
Create unified data access services that expose the digital thread to stakeholders across the organization, breaking down silos between engineering, manufacturing, operations, and business teams
Implement data quality frameworks and lineage solutions that ensure accuracy across diverse data types while illuminating information flow through complex systems—essential for aerospace compliance, continuous improvement, and establishing the trusted data foundation required for advanced analytics and AI
Partner with cross-functional teams to strengthen the digital thread through DataOps practices and champion data governance standards that keep our diverse data assets trusted, accessible, and valuable
Actively contribute to a collaborative team environment through code reviews, knowledge sharing, and constructive feedback that continuously improves our data solutions and maintains high engineering standards

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Science, or related technical field (or equivalent practical experience)
3+ years of professional experience in data engineering or related field
Hands-on experience with semantic technologies, knowledge graphs, or linked data systems
Experience designing and implementing ontologies, taxonomies, or semantic data models
Proficiency with modern data engineering tools and languages (e.g., Python, SQL)
Experience with graph databases (e.g., Neo4j, Amazon Neptune, Stardog) and/or triple stores
Strong understanding of data integration patterns and API design
Excellent communication skills with ability to translate technical concepts for diverse audiences
Experience with cloud platforms (preferably AWS)
Experience with DataOps practices and modern data platform architectures (cloud and hybrid)
Understanding of DevOps practices including CI/CD, version control (Git), and infrastructure as code
Ability to work collaboratively in a fast-paced environment
Exceptional troubleshooting skills with the ability to spot issues before they become problems.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringsemantic technologiesknowledge graphsontologiestaxonomiesdata modelsPythonSQLgraph databasesdata integration patterns

Soft Skills

communication skillscollaborationtroubleshootingconstructive feedbackknowledge sharingteam environmenttranslating technical conceptscross-functional teamworkcontinuous improvementengineering standards

Certifications

Bachelor's degree in Computer ScienceBachelor's degree in Information SystemsBachelor's degree in Data Science