Enterprise Data Strategist

Flipside 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.

### 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

### 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

### 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.