The future of data lies in decentralization, and the concept of a data mesh is the proven approach for implementing this at Enterprise scale. We’re here to make it a reality. Nextdata OS is a data-mesh-native platform built to meet the challenge of decentralizing data at scale. We are inventing a new way for developers to work with data and share it responsibly via data product containers.

Our vision is to build a world where AI/ML and analytics are powered by decentralized, responsible, and equitable data ownership, across boundaries of organizations, technology, and most importantly boundaries of trust.

Our purpose is to change the experience of creating, sharing, discovering, and using data forever, to be connected, fast, and fair based on data mesh principles.

Our technology is designed to empower data developers, users and owners with a delightful experience where data products are a first-class primitive, with trust built-in.

We are here to accept the reality that the world of data is complex and messy; data models are out-of-date the moment they are created; data is owned across trust boundaries; data is stored on different platforms; data is used in many different modes and most importantly data can't protect itself. We recognize that past approaches to tackle these complexities with centralized data collection, modeling and governance are ineffective at best and pathologically unfair at worst. Our mission is to reimagine the world of data with you.

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The Principal AI Platform Engineer at Nextdata designs and builds the interfaces, systems, and agents that make governed enterprise data usable by both humans and AI agents.

This role sits at the intersection of data engineering, AI engineering, distributed systems, and product architecture. You will help define how autonomous data products expose their semantics, contracts, policies, metadata, and outputs to AI systems through agentic interfaces such as MCP-compatible endpoints, typed APIs, semantic tools, and data agents.

You will not just build pipelines for AI models. You will build the product capabilities that allow AI systems to discover the right data, understand its meaning, request access, execute safe actions, and return reliable answers with context, lineage, and policy enforcement.

• Design agentic data interfaces that let AI agents discover, understand, and safely use data products.
• Build MCP-compatible endpoints, tools, and APIs for governed AI/data access.
• Develop data agents that reason over metadata, semantics, contracts, policies, and data outputs.
• Make data products AI-ready across SQL, documents, vectors, graphs, APIs, and semantic models.
• Build safe query and action flows with access checks, policy enforcement, approvals, and audit trails.
• Work on retrieval, semantic search, tool selection, context construction, and answer grounding.
• Define reusable patterns for agent-readable metadata, structured outputs, observability, and evaluation.
• Partner with product, engineering, and customer teams to turn enterprise AI/data use cases into product capabilities.

• Strong experience in data engineering, data platforms, distributed systems, or enterprise data infrastructure.
• Practical experience building AI-enabled data systems, retrieval systems, semantic layers, or data agents.
• Strong knowledge of SQL, APIs, documents, vector search, knowledge graphs, and metadata systems.
• Experience with agentic interfaces, tool-calling, MCP or similar protocols, function calling, or AI backends.
• Good understanding of governance: access control, policies, contracts, lineage, data quality, PII protection, and auditability.
• Ability to build production systems that are safe, observable, testable, and reliable.
• Strong Python skills and comfort working across backend services, data systems, APIs, and AI frameworks.
• Product-minded judgment: you know the difference between a demo, a customer-specific workaround, and a reusable platform capability.
• Comfort working in ambiguous areas where the patterns are still being defined.

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Nice to have

• Experience with data mesh, data products, semantic models, catalogs, governance platforms, or data marketplaces.
• Experience with MCP servers, tool registries, LLM orchestration, RAG systems, or multi-step agents.
• Experience with Databricks, Snowflake, BigQuery, Spark, DuckDB, Postgres, graph databases, vector databases, or lakehouse architectures.
• Experience with enterprise identity and authorization systems such as SSO, OAuth, OIDC, SAML, SCIM, RBAC, ABAC, or policy engines.
• Experience evaluating AI systems for retrieval quality, tool-use accuracy, groundedness, reproducibility, and failure modes.

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We are an early-stage company, but we operate like a mature team. Many of us have families, and we value stability.

We offer:

• $2,000 home office setup budget
• Comprehensive health, vision, and dental coverage for you and your family
• Competitive salary and meaningful early-stage equity