The Data Enablement Engineer plays a critical role in transforming enterprise data into trusted, scalable, and business-ready data products that power analytics, decision-making, and next-generation AI capabilities across the organization.
Operating at the intersection of data engineering, analytics, and business engagement, this role is responsible for shaping how data is modeled, curated, and consumed within the enterprise. You will partner closely with business stakeholders, analysts, and engineering teams to design clean, intuitive, and high-value data assets that enable self-service analytics and accelerate organizational insight.
A unique and growing aspect of this role involves building trusted data environments for AI and agentic systems. Rather than exposing raw or loosely governed data, you will help create purpose-built, well-scoped data products that allow AI-driven solutions to operate confidently against reliable and contextually accurate information. This work will directly influence how the organization scales modern analytics and AI responsibly.
The ideal candidate enjoys solving ambiguous problems, engaging directly with business users, and simplifying complex data challenges into elegant, governed, and reusable solutions. Success in this role requires a blend of technical depth, strong communication skills, curiosity, and a passion for making data more accessible, trustworthy, and impactful across the enterprise.
What You'll DoData Product Development · 45%-
Design and build business-facing data views and marts in Snowflake that serve analysts, domain stakeholders, and operational use cases. Clean, well-scoped, and documented enough to be trusted without hand-holding
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Design and maintain curated data views that serve as trusted, walled-garden assets for agentic AI systems, scoped precisely so an AI agent can operate confidently against governed, unambiguous data without exposure to raw or ill-defined upstream sources
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Experiment with and operationalize Snowflake Semantic Views, MCP service capabilities, and trusted queries as they mature into production-ready tools on our platform
Stakeholder Collaboration · 35%-
Partner directly with business analysts, BI developers, data engineers, finance, sales ops, and domain leads to gather data requirements and validate solutions
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Facilitate working sessions to surface implicit business rules, document them explicitly, and encode them into durable data products
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Serve as a translator between platform capabilities and business needs — explaining what is possible, what is governed, and what tradeoffs exist
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Support data enablement programs that help business users get more value from self-service analytics tools
Governance & Data Quality · 20%-
Apply data governance frameworks to ensure data products are documented, trustworthy, and appropriately access-controlled
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Maintain clear, business-friendly documentation for all owned data assets so consumers understand definitions, grain, and appropriate use