Data Platform Engineer | Digital Operations

We are seeking a Platform Engineer to join our Data and AI Platform team, bringing strong hands-on engineering skills and a builder's mindset to help design, deploy, and operate the data and knowledge infrastructure that powers our organization.  In this role, you will combine software and data engineering fundamentals with practical experience in cloud-native architectures, graph databases, and API design to build robust, scalable production systems that enable rich data discovery, seamless integration, and reliable access across diverse data sources. You will work collaboratively with data scientists, other platform engineers, and domain experts to evaluate, architect, and implement the most appropriate data solutions, and then roll up your sleeves to build, deploy, and maintain them in production. The ideal candidate is a pragmatic, hands-on technologist who thrives on solving real infrastructure and data challenges end-to-end, from initial design through production operations, and who takes pride in building systems that make data more findable, accessible, and reusable across the enterprise.

• Design, build, and operate scalable data platform services and frameworks that empower domain teams to manage their data assets within a cohesive enterprise architecture
• Implement data integration solutions that create a digital thread connecting information across the entire product lifecycle from design through manufacturing to field operations
• Build and maintain data storage systems including data lakes, warehouses, and graph databases that make organizational data accessible and ready to power advanced analytics and AI
• Create and extend enterprise data catalog capabilities that document what data exists across organizational systems, how it is structured, and how it connects, enabling teams across the company to navigate the full data landscape
• Develop and expose performant data access layers using technologies such as GraphQL and REST APIs, providing intuitive interfaces to complex underlying data
• Deploy, monitor, and manage production data systems on cloud infrastructure, ensuring high availability, performance, and reliability
• Implement data quality and lineage solutions that ensure accuracy and traceability across diverse data types; essential for aerospace compliance and establishing a trusted foundation for analytics and AI
• Build and maintain CI/CD pipelines, infrastructure as code, and automated deployment processes that keep our data platform agile, reproducible, and production-ready
• Partner with cross-functional teams on DataOps practices and data governance to keep data assets trusted, accessible, and well-managed.
• 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

• Bachelor's degree in Computer Science, Information Systems, Data Science, or related technical field (or equivalent practical experience)
• 3+ years building and shipping data or platform infrastructure in production, with ownership of system availability, performance, and incident response
• Strong software engineering fundamentals including clean code practices, testing, and building maintainable systems
• Provisioned, configured, and operated cloud-based data services in production (preferably on AWS)
• Built or maintained systems using graph databases (e.g., Neo4j, Amazon Neptune, Stardog) and designed graph data models
• Designed and built APIs that other teams or systems depend on, with working knowledge of GraphQL
• Proficiency with modern data engineering tools and languages (e.g., Python, SQL)
• Designed and built data pipelines and data models that ingest and transform data from diverse sources to serve multiple consumers or use cases
• Built and maintained CI/CD pipelines, infrastructure as code, and containerized deployments (e.g. Docker, Kubernetes) for production systems
• Strong troubleshooting skills with the ability to diagnose and resolve issues across the full stack from infrastructure through application layers
• Excellent communication skills with the ability to translate technical concepts for diverse audiences

• 5+ years of experience building and operating production data platforms at scale
• Experience with semantic technologies including knowledge graphs, ontology design, and related standards (e.g., RDF, OWL, SPARQL, SHACL, RML/R2RML)
• Familiarity with AI-assisted development tools such as agentic coding assistants, gained through practical use
• Experience with distributed computing frameworks (e.g., Spark) and/or event-driven and streaming architectures (e.g., Kafka, Kinesis)
• Hands-on experience with metadata management platforms and enterprise data catalogs
• Implemented data lineage and data quality solutions at scale
• Experience with monitoring, observability, and alerting for production data systems
• Experience working in regulated industries or with complex data governance frameworks
• Familiarity with aerospace or manufacturing data standards and regulations
• Knowledge of IoT protocols and time-series data management