Principal Forward Deployed Engineer

CData Software

2d•Remote

About The Position

CData is the data layer behind enterprise AI in production. As organizations move from experimentation to mission-critical agentic workflows, they need a partner who can connect AI to live, governed data across hundreds of systems — and an architect who can design that solution inside the customer's environment. We're hiring an Architect, Forward Deployed Engineering (FDE) to be the senior technical leader inside our most strategic enterprise engagements. You'll embed with customers to translate AI ambitions into production architectures on CData Connect AI, our managed MCP platform, and the broader CData stack — designing how data, AI, and governance fit together end-to-end. You'll be the technical voice the customer trusts and the field-level expert whose patterns shape what we build next in product. This is a senior IC role with significant leverage. You won't manage people directly, but you'll mentor FDEs on each engagement, set the technical bar for the work, and partner closely with the FDE Director and Product leadership on the patterns that turn field wins into reusable IP. Why This Role Matters: The Architect, Forward Deployed Engineering works directly with strategic customers and designs how AI, data and governance come together end-to-end to ensure deployments on Connect AI are scalable, secure, and deliver measurable outcomes. This role goes beyond solution design. By shaping architectures, defining best practices, and identifying product gaps in real time, the Architect directly influences the evolution of the Connect AI platform. Their work creates patterns that accelerate future deployments, strengthen technical credibility with enterprise buyers, and reduce time to value across engagements.

Requirements

7+ years as a Solutions Architect, Principal SE, Forward Deployed Engineer, or Technical Lead at a data platform, AI, or enterprise SaaS company
Has built and shipped production AI applications — not just prototypes
Worked on a SaaS Platform in an Architect Profile (or closely aligned role)
Customer-facing track record with senior technical buyers and architecture review boards
Strong AI/ML literacy: LLM capabilities, agentic architectures, RAG patterns, prompt engineering, and when to apply each
Able to define Multi-tenant Architectural patterns and security objectives
Hands-on enterprise data integration: SQL, ETL/CDC pipelines, API design, ODBC/JDBC, and multi-source connectivity
Able to design governed data access for AI agents — RBAC, OAuth 2.1, semantic scoping, and audit-trail requirements
Experience with modern data stacks (Snowflake, Databricks, Salesforce) and cloud-native deployment patterns
Ability to translate complex AI + data concepts into executive-ready architecture proposals
High agency; comfortable being the senior technical voice in the room with the customer
Experience mentoring junior engineers without requiring direct reporting relationships

Nice To Haves

Direct experience with the MCP protocol and AI agent frameworks (LangChain, CrewAI, Copilot Studio)
Prior experience as an FDE or in a similar embedded customer-facing engineering role

Responsibilities

Lead customer engagements
Run technical discovery workshops with customer architects, data leaders, and AI teams, mapping data sources, MCP Workspace scoping, and agent tooling requirements
Own the scoping for AI deployments with clear acceptance criteria around agent accuracy, data coverage, and governance
Translate complex AI + data concepts into executive-ready architecture proposals; defend trade-offs to CxO-level stakeholders
Architect AI data connectivity end-to-end
Design solutions across Connect AI’s 350+ sources covering MCP server configuration, semantic context modeling, and governance integration
Architect agent orchestration using Connect AI Toolkits, defining which data, schemas, and actions each agent can access in production
Design governed access patterns for AI agents: RBAC, OAuth 2.1, semantic scoping, and audit-trail requirements
Define AI Best practices using agents, skills and LLMs to drive successful customer outcomes
Shape the evolution of the Connect AI platform through real-world deployments
Identify architectural and product gaps during live enterprise engagements and partner with Product and Engineering to define scalable solutions
Author technical specifications and implementation recommendations for Connect AI enhancements, including both features and core architectural improvements
Build reusable reference architectures, deployment patterns, and MCP blueprints that reduce implementation friction and accelerate future customer deployments
Translate recurring customer deployment challenges into scalable platform capabilities and architectural standards
Elevate the team
Mentor FDEs on AI integration patterns, semantic data modeling, and customer-facing technical delivery
Serve as the first level technical escalation point for customer engagements
Partner with Product, Engineering, Solutions Engineering, and Customer Success on architectural standards
Codify field patterns into shared assets, playbooks, templates, sample agents, that the broader team can reuse (SE, CS, support etc.)