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edisyl builds AI solutions that transform messy institutional data into actionable decisions and workflows for enterprises. The company emerged from 8 years of blockchain data infrastructure work (20+ chains, 700M+ resolved wallets) and now applies that capability to help enterprises scale data operations without armies of analysts. They have active deployments with financial institutions and proven architecture, and are now building the enterprise go-to-market motion.
As Enterprise Data Strategist, you are the first point of substantive contact with new clients. You diagnose their actual data problems—not the stated ones—within two conversations. You understand data environments, team structures, and organizational friction. You scope engagements around specific pain points and named stakeholders, avoiding generic AI evaluations or pilot programs designed to fail.
You lead data strategy engagements assessing current state, defining target architectures, and developing phased roadmaps toward AI activation. You run executive-level workshops aligning business objectives with data and AI investment priorities. You define data governance, quality, and readiness frameworks that accelerate customer value realization. You partner with Forward-Deployed Engineers to translate strategic intent into executable plans, identify expansion opportunities by connecting latent data assets to new use cases, and codify methodology through thought leadership.
Success in year one means leading strategy engagements across multiple enterprise accounts with at least two moving from assessment into active deployment. You build a repeatable data maturity framework and engagement model. Clients request the next phase before the current one concludes. Success is measured by whether something changed in the client's business, not whether the strategy was elegant.
You bring 6–10 years combining data strategy with direct client or executive advisory exposure—senior engagement manager or principal-level at a data/management consulting firm, or director-or-above in a large enterprise data org. You've run executive workshops, translated ambiguous business needs into structured data requirements, and understand modern data architecture (data mesh, lakehouse, real-time vs. batch, governance). Experience in financial services, insurance, or crypto/blockchain infrastructure is strongly preferred.
You have sharp diagnostic instincts, comfort with ambiguity, outcome orientation, and strong opinions on enterprise AI requirements versus vendor promises. Bonus experience includes forward-deployed or consultative advisory backgrounds (McKinsey Data, Palantir, Databricks professional services) or direct work with founders in small-company contexts.
