Enterprise Data Architect

Who This Is For Enterprise customers rarely know what they actually need. They know something isn't working — the data exists but doesn't flow, the AI initiative stalled, the analysts are still running everything manually. They can describe the symptom. They can't always name the cause. You can. Within two conversations, you know where the real problem is. Not the stated one — the structural one. And you know how to turn that into a roadmap a CTO will fund and a data engineering team will actually follow. You've watched an AI initiative fail because the data wasn't ready. You understood exactly why while everyone else was blaming the model. You're not looking for a defined methodology to execute. You're looking for the chance to build one. About edisyl edisyl builds AI solutions that turn messy institutional data into decisions, workflows, and outcomes. We came out of blockchain data infrastructure — 8 years, 20+ chains, 700M+ resolved wallets — and now deploy that capability to enterprises navigating the same challenge: how to make their data work for them at scale, without armies of analysts. We have active deployments with a finanical institution and Interlochen, a proven architecture, and inbound from firms that need what we've built. The technology works. What we're building now is the enterprise motion around it. The Role You are the first person a new client has a real conversation with. You understand their data environment, their team structure, their actual friction — not their stated problem. You scope engagements around the named person with the named pain. No generic AI evaluations. No pilot programs designed to go nowhere. You stay involved through delivery, making sure what gets built matches what was promised — and that what was promised was the right thing to promise. This is a senior advisory role. You operate with significant autonomy, shape methodology, and influence how edisyl goes to market with its largest customers. What You'll Actually Do Lead data strategy engagements with enterprise customers — assessing current state, defining a target architecture, developing phased roadmaps toward AI activation Run executive-level workshops to align business objectives with data and AI investment priorities Define data governance, quality, and readiness frameworks that help customers get value from edisyl faster Partner with Forward-Deployed Engineers to translate strategic intent into executable implementation plans Identify expansion opportunities by connecting latent data assets to new AI use cases Codify methodology and contribute to edisyl's market positioning through thought leadership What Success Looks Like in Year One You've led strategy engagements across multiple enterprise accounts and at least two have moved from assessment into active deployment. You've built a data maturity framework and engagement model the team can run repeatedly — not just describe. The clients you've worked with are asking for the next phase before the current one is done. The measure isn't whether the strategy was elegant. It's whether something changed in the client's business because of it. Compensation Competitive base salary, meaningful early-stage equity, and a variable component tied to the engagements you lead and the expansions you drive. We'll be transparent about the full picture in our first conversation. Who We're Looking For Experience 6–10 years combining data strategy with direct client or executive advisory exposure — senior engagement manager or principal-level at a data or management consulting firm, or director-or-above inside a large enterprise data org You've run executive-facing workshops and translated ambiguous business needs into structured data requirements Strong grasp of modern data architecture: data mesh, lakehouse, real-time vs. batch, governance frameworks Experience in at least one priority vertical — financial services, insurance, or crypto/blockchain infrastructure — strongly preferred The Stuff That's Harder to Teach Sharp diagnostic instincts. You walk into a new environment and find the real problem fast — not the one in the RFP. Comfort with ambiguity. Enterprise data environments are not clean. Neither are the conversations around them. Outcome orientation. You measure success by whether something changed in the client's business, not whether the engagement was delivered on time. Strong opinions. You have a clear view on what enterprise AI actually requires versus what vendors promise — and you've been in the room when the gap became undeniable. Bonus (Genuinely Not Required) Background at a firm known for forward-deployed or consultative advisory — McKinsey Data, Palantir, Databricks professional services, or similar Experience working directly with a CEO or founder in a small-company or build-out context Familiarity with blockchain data, DeFi, or institutional crypto infrastructure Why This, Why Now edisyl is at the moment where the technology is proven and the enterprise market is ready. The person who takes this role will shape how edisyl's largest customers understand their own data problems — and what it looks like to solve them. That methodology doesn't exist yet. You'll be the one who builds it. That's a rare place to learn and a real chance to have outsized impact. #J-18808-Ljbffr