Revenue Solution Architect

Grade Level (for internal use): 12 Solutions Architect, AI & Delivery Enablement (Energy)   About the Role   The Solutions Architect sits within the Energy organization as a technical delivery expert bridging customers, internal teams, and product development. Unlike a commercial pre- sales   role, this position carries no sales quota. Instead, it focuses on machine delivery, AI solutions support, integration design, and technology enablement—both customer-facing and internal.   You will translate energy business workflows into implementable AI and data solutions, help customers explore our datasets to answer key market questions, and serve as an internal engine for AI education and enablement across commodities. You will work closely with Business Line SMEs, Commercial, Technology, and Product teams without owning any one of their mandates—you   connect   them.   Primary Focus   Educate customers on Energy business workflow possibilities, shifting conversations from data feeds and features toward outcomes and repeatable workflows.   Map customer   objectives   to approved product capabilities, datasets, and integration patterns, producing clear solution designs and technical validations that reduce delivery risk.   Learn and understand our data endpoints and use cases,   identifying   when to highlight existing workflows and when to develop new workflows and prompts.   Support prototypes and POCs by   validating   feasibility and documenting results, limitations, and reusable patterns.   Customer Enablement (No Quota)   Partner with customers in technical discovery,   requirements   clarification, and solution shaping   grounded   in energy market use cases.   Help customers connect key market themes to our   datasets ,   demonstrating   how to explore questions through our data and AI tooling.   Resolve technical questions and adoption blockers with evidence-based responses grounded in platform capability and prior implementations.   Internal Enablement & AI Education   Run point on internal AI training—steering cross-commodity AI education so teams across   the Energy organization   build practical capability.   Contribute to playbooks, documentation, prompt libraries, and training materials that scale AI and workflow knowledge   division-wide .   Standardize technical discovery and architecture documentation through consistent use of approved formats.   Cross-Functional Collaboration   Collaborate with Business Line SMEs, Commercial, Technology, and Product to ensure proposed solutions are deployable, supportable, and aligned to current standards.   Support product development with structured feedback from customer and delivery learnings (pattern gaps, recurring requirements, common integration issues)—informing the roadmap without owning product management.   Coordinate inputs across a matrixed environment to meet fast-paced timelines.   Technical Delivery & AI Engineering   Leverage the Databricks ecosystem to federate data across heterogeneous sources—via Lakehouse Federation, Unity Catalog governance, and external connections—and   stand up   Genie MCP proof-of-concepts that expose curated semantic layers to natural-language querying.   Design and build advanced agentic AI " skills"— modular, composable workflow components that encapsulate domain logic into reusable, parameterized capabilities.   Implement and enable MCP (Model Context Protocol) server integrations within new AI solutions, extending tool-calling surface area and orchestrating secure access to data endpoints, APIs, and downstream systems.   Diagnose and resolve issues across the delivery stack—API latency and error handling, cloud infrastructure, authentication, and pipeline reliability—instrumenting for observability to   maintain   stability and   performance   SLAs.   Architect token-management and context-optimization strategies (context-window budgeting, prompt