Principal Engineer (Data Platform)

Atlan is a company focused on creating the missing context layer for data and AI, aiming to help enterprises close the AI value chasm. They are seeking a Principal Engineer to architect and scale their foundational data platform, responsible for designing and building platform services at enterprise scale and driving technical standards.

Responsibilities:

• Design and build platform services—APIs, infrastructure components, runtime systems, and ingestion frameworks—at enterprise scale
• Architect the context store that transforms lakehouse infrastructure into AI-ready systems with multimodal capabilities (structured, unstructured, vector, graph)
• Solve complex multi-tenant isolation and scaling problems for enterprise SaaS
• Design data contracts governing ingestion, validation, processing, routing, storage, and serving across heterogeneous systems
• Own critical shared infrastructure including lakehouse (Iceberg/Polaris), vector stores, graph databases, and OLTP systems
• Drive technical standards through RFCs, architecture reviews, and documentation
• Mentor senior engineers and influence architecture decisions across teams
• Write production code using AI-assisted development tools (Claude Code, Cursor)
• Debug distributed systems issues across Kubernetes, workflow orchestration, and microservices

Requirements:

• 8+ years in platform engineering, infrastructure, or backend systems at a SaaS company
• Experience building enterprise-scale distributed systems at scale
• Deep expertise in multi-tenant architectures and tenant isolation strategies
• Strong Kubernetes, containerization, and cloud infrastructure skills (AWS/GCP/Azure)
• Hands-on experience with distributed systems patterns—service mesh, event-driven architecture, orchestration
• Track record of driving multi-quarter technical initiatives from concept through production at scale
• Experience designing contract-driven or schema-first data platforms
• Familiarity with Temporal or similar workflow orchestration systems
• Data quality frameworks, observability systems, and cost attribution at scale
• Experience supporting enterprise workloads with strict compliance requirements
• CI/CD pipeline design and GitOps practices