Principal Engineer (Data Platform)

Principal Engineer (Data Platform) WHO WE ARE Atlan is building the missing context layer for data and AI, helping enterprises close the AI value chasm. Today, 95% of AI pilots fail because AI systems don’t understand the context behind data: what it means, how it’s governed, and how it should be used. Atlan connects to every part of the modern data and AI stack to unify this context into a single, shared layer that both humans and AI agents can rely on. With Atlan, teams can discover, understand, and trust their data; build and collaborate on a shared body of knowledge; and activate that context across analytics, operations, and AI workflows.Trusted by global enterprises like Mastercard, Workday, General Motors, Unilever, Ralph Lauren, FOX, Nasdaq, and Medtronic, we’re backed by world-class investors including GIC, Insight Partners, Meritech, Peak XV, and Salesforce Ventures About the Role Atlan is building the context layer for AI agents and applications - transforming how data platforms power the next generation of AI. We're looking for a Staff and Principal Engineer to help architect and scale the foundational data plane that makes it easy to build apps, agents, and solutions for the AI era. You'll work on systems handling billions of assets, serving 100K+ users, with 99.99% availability targets. This is a high-agency role where you'll define technical direction, drive multi-quarter initiatives, and pioneer AI-native development practices. What you will do 🤔 - 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 What makes you a match? 😍 Must Have - 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 Deep Expertise in One or More - Lakehouse architectures - Vector stores - Graph databases - Streaming systems Strong to Have - 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 Who You Are - You embrace AI-native development and want to pioneer new engineering workflows - You have high agency and take ownership of ambiguous problems - You're a strong async communicator who can influence without authority - You're comfortable with fast-changing priorities in a scale-up environment - You act as a force multiplier—elevating the technical bar for those around you Team & Location (Remote) You'll join a distributed team (India/Austra ... (truncated, view full listing at source)