Enterprise Data Strategist

About the role

This position is for an Enterprise Data Strategist at Flipside, a company that leverages its background in blockchain data infrastructure to create AI solutions for enterprise clients. The core responsibility is to lead data strategy engagements, which includes assessing a customer's current data landscape, defining a future-state architecture, and creating phased roadmaps to integrate AI. This work is crucial for helping large organizations make their data actionable without needing extensive internal analyst teams.

A key aspect of the role involves facilitating executive-level workshops to align business goals with data and AI investment priorities. The strategist will also be responsible for establishing data governance, quality, and readiness frameworks to maximize the value customers derive from Flipside's platform. Success in this role means not just delivering engagements on time, but ensuring tangible business changes for the client, indicating an outcome-oriented approach.

Collaboration with Forward-Deployed Engineers is essential to translate strategic plans into executable implementations. The strategist will also identify new opportunities by connecting existing data assets with potential AI use cases. Beyond client work, the role contributes to Flipside's market positioning by codifying methodologies and engaging in thought leadership, shaping how the company's AI solutions are perceived in the market.

The salary for this role ranges from $146,000 to $250,000 USD, in addition to meaningful early-stage equity and a variable component tied to engagement performance.

Skills that matter here

• data strategy: This role centers on leading data strategy engagements for enterprise customers, from assessment to roadmap development.
• client or executive advisory: The position requires direct experience in advising clients or executives, including running workshops and translating business needs.
• modern data architecture: A strong understanding of concepts like data mesh, lakehouse, and governance frameworks is necessary for defining target architectures.
• financial services, insurance, or crypto/blockchain infrastructure: Experience in one of these priority verticals is preferred, indicating a need for industry-specific data knowledge.
• data governance, quality, and readiness frameworks: The strategist will define these frameworks to help customers derive value from their data and AI investments.
• AI activation: A key objective is developing phased roadmaps that lead to the successful activation and integration of AI solutions for customers.

Who this role suits

• A person with 6-10 years of experience in data strategy combined with direct client or executive advisory will thrive here.
• Someone who can quickly diagnose the real problem in complex enterprise data environments, rather than just addressing surface-level requests.
• An individual comfortable with ambiguity and messy data, capable of structuring unclear business needs into actionable data requirements.
• A professional who measures success by tangible business outcomes and has strong, informed opinions on enterprise AI requirements.

From the employer

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.