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Enterprise Data Strategist
8.0/10
Flipside
$146,000 – $250,000 USD
Remote
senior
about 1 month ago
May be outdated
aicryptofintechweb3
AI Summary
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Description
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
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.
Requirements
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
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