Solution Architect

Role details

Solution Architect Sigma Computing The SA role has evolved. Here’s the version we’re hiring for. The SA job in 2026 is not the SA job in 2023. Three things now sit at the center of how we evaluate this role. This hire has to do all three at a senior level, with the architectural depth to back it up. 1. Use AI every day to do the job better. If you are not using Claude, ChatGPT, Cursor, or equivalents to accelerate your account prep, architecture diagramming, prototype builds, RFP responses, and discovery synthesis, you are getting outworked by SAs who are. We expect this hire to treat AI tooling as default infrastructure, not novelty. Come with a point of view on what you run, why, and how you use it to compress weeks of work into days. 2. Sell AI into the account. Buyers want to talk about agents, MCP, A2A, context engineering, and which model is powering what. You have to be fluent. You know Sigma’s AI surface cold: Sigma Assistant in build, analyze, and plan modes, AI functions, input tables with LLM enrichment, MCP integration, and warehouse-native agent patterns. You can architect Sigma agents and warehouse agents into a customer’s stack and explain the tradeoffs to a head of data and a CISO in the same call. You also speak credibly about Claude, OpenAI, Gemini, and the broader stack the customer already runs. 3. Sell against AI. Every enterprise deal has AI competition in it. Sometimes it is Databricks Genie. Sometimes it is Snowflake Cortex Analyst. Sometimes it is a systems integrator pitching a bespoke agent built over the weekend. You know where each of these breaks at scale, where Sigma’s warehouse-native architecture wins on governance, freshness, and cost, and how to draw the line for a skeptical CDO without hand-waving. You can defend that position in an architecture review, on a security questionnaire, and across three follow-up calls. About Sigma Sigma is the AI runtime environment for the modern enterprise. Teams build apps, agents, and analytics directly in Sigma, with governance and security inherited from the cloud data warehouse. No extracts, no separate AI pipelines, no shadow stack to maintain. About the role Solution Architects are the senior technical voice on our Solution Engineering team. SAs partner with SEs on the most complex enterprise deals: architecting solutions, leading deep technical conversations, and unblocking opportunities that hinge on data infrastructure, security, or AI strategy. Your depth compounds the work SEs are already doing and accelerates deal velocity across the territory. You will work alongside Enterprise Regional Sales Managers and SEs on new and existing accounts. You will partner closely with Sales, Product, Engineering, and Support. Prospects and customers will come to you for architectural guidance and product expertise, especially on AI strategy, governance, and warehouse-native architecture. What you’ll do Lead the technical strategy on complex enterprise opportunities, paired with the SE assigned to the account. Run deep technical discovery and architecture workshops with data teams, security teams, AI leads, and executive stakeholders. Design and build custom prototypes that prove out high-value use cases, including AI-driven workflows using Sigma Assistant, Sigma agents, warehouse agents, and MCP integrations. Present Sigma’s architecture and AI runtime story to audiences ranging from analysts to CTOs and CDOs. Own the technical narrative on RFPs, RFIs, AI risk reviews, and security questionnaires. Advise on integration, migration, governance, and AI patterns across Snowflake, Databricks, BigQuery, and Redshift. Position Sigma against Databricks AI/BI and Genie, Snowflake Cortex Analyst, Tableau, Power BI, Looker, and AI-native entrants. Defend that position with architecture, not slogans. Build reusable SA assets: architecture patterns, AI-workflow playbooks, competitive teardowns, and reference implementations the whole team can run. Shape the product from the front line. File feature requests, write up customer patterns, and partner with Product and Engineering on what to build next, especially across the AI surface. Mentor SEs. Contribute to the wiki, the playbooks, and the next hire’s ramp. Manage several enterprise engagements at once. Earn and maintain product, sales, and technology certifications. Hit quarterly and annual targets set by your manager. What we’re looking for Technical depth. 8+ years in business intelligence, analytics engineering, or data platform roles, with at least 3 in a customer-facing technical role (SE, SA, or consulting). Deep expertise in at least one cloud data warehouse: Snowflake, Databricks, BigQuery, or Redshift. Strong SQL and a solid grasp of modern data architecture: warehousing, modeling, governance, security. Data engineering fluency. ETL and transformation experience with dbt, Fivetran, Matillion, or comparable tools. AI fluency. Daily user of modern AI tools. Comfortable talking about agents, MCP, A2A, context engineering, retrieval, evals, and the major model providers. You can position Sigma’s AI stack against warehouse agents like Genie and Cortex Analyst, and against AI-native BI entrants, without hand-waving. Enterprise selling. Track record of leading complex enterprise sales cycles or large BI implementations. You know how to partner with AEs and SEs to close. Executive presence. You hold a CFO and a data engineer in the same room without switching gears awkwardly. You lead the architecture review and the boardroom briefing. Pace and energy. You operate well in a high-velocity environment. Self-starter. No hand-holding. Team fit. You want a team that sharpens each other. You bring field intel back. You contribute to the playbooks and the next SE’s ramp. Ego stays out of the room. Education. Bachelor’s degree in a technical field, or equivalent experience. Travel. Willingness to travel up to 25%. Additional Job details The base salary range for this position is $135k - $180k