Principal Data Engineer, Upbound

• 10+ years of software/data engineering experience with at least 4 years in technical leadership roles
• Proven track record building data platforms that support production systems at scale
• Deep expertise in both traditional data engineering (Spark, Airflow, data lakes) and ML-specific infrastructure (feature stores, model serving)
• Experience with vector databases (Pinecone, Weaviate, Qdrant, Milvus, pgvector, Opensearch, ElasticSearch)
• Demonstrated experience with LLM applications, including RAG architectures and semantic search implementations
• Understanding of Kubernetes, cloud-native architectures, and infrastructure-as-code principles
• Strong understanding of data requirements for AI/ML systems: training pipelines, feature stores, and inference infrastructure
• Hands-on experience building knowledge bases and semantic search systems for technical documentation and code
• Experience with embedding models for code and technical documentation
• Knowledge of time-series data processing for infrastructure metrics and events
• Understanding of graph databases and their application to infrastructure dependency modeling
• Exceptional technical judgment with the ability to navigate both the AI and cloud-native landscapes
• Demonstrate a positive attitude and foster an environment of experimentation and innovation
• Strong ability to translate infrastructure management problems into data engineering solutions
• Passion for making infrastructure management more intelligent and accessible through AI
• Deep empathy for platform engineers and understanding of their operational challenges

Desirable

• We're seeking an exceptional Principal Data Engineer to serve as the technical leader for data infrastructure supporting Upbound's current product suite in addition to our AI initiatives and intelligent control plane capabilities
• In this role, you'll architect and drive the development of sophisticated data platforms that power AI-driven features
• You'll design solutions that leverage control planes and our Marketplace as a knowledge store, building RAG systems and semantic search capabilities that help users discover and implement infrastructure patterns at scale
• You'll create data pipelines that process infrastructure telemetry, configuration data, and usage patterns to train models that make our control planes more intelligent and autonomous
• This is an opportunity to work at the intersection of cloud-native infrastructure and artificial intelligence, directly impacting how enterprises manage their infrastructure through Upbound's platform
• Define and drive the technical vision for data platforms that support AI-powered features in Crossplane and Upbound Spaces
• Lead the design of data pipelines that transform infrastructure and data into training datasets for ML models
• Architect vector search and RAG systems that leverage Crossplane Control Planes & Upbound Marketplace as a knowledge store
• Build data infrastructure that processes resources, extensions, and compositions for semantic search
• Establish frameworks for collecting, processing, and analyzing infrastructure configuration data
• Design data pipelines that handle Crossplane-specific data
• Create infrastructure for indexing and searching Upbound Marketplace content, documentation, and community patterns
• Develop metrics and monitoring for AI features integrated with Upbound's control plane architecture
• Design data systems that power AI agents for infrastructure provisioning & operations, helping users generate and optimize Crossplane compositions
• Create feature engineering platforms that extract signals from control plane operations, resource status, and reconciliation patterns
• Implement data infrastructure for training models that predict infrastructure failures, optimize resource allocation, and suggest configuration improvements
• Drive the development of knowledge graph representations of infrastructure dependencies and relationships