Position Responsibilities:
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 Do:
Data 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
- 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
- Experiment with and operationalize Snowflake Semantic Views and trusted queries as they mature into production-ready tools on our platform
- Serve as one of the team's early practitioners for Model Context Protocol (MCP) data interfaces in Snowflake as that capability comes online, translating platform architecture into AI-consumable data contracts
- Leverage your understanding of BI semantic model patterns (Power BI tabular models, Tableau Data Extracts, or equivalent) to inform how views and marts are structured.
- Iterate rapidly with stakeholders through multiple feedback cycles, refining data products until they accurately encode business logic and are ready for self-service consumption
- Expose structured and unstructured data sources in ways that are immediately useful to non-technical consumers
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
- Facilitate working sessions to surface implicit business rules, document them explicitly, and encode them into durable data products
- Serve as a translator between platform capabilities and business needs — explaining what is possible, what is governed, and what tradeoffs exist
- 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
- Maintain clear, business-friendly documentation for all owned data assets so consumers understand definitions, grain, and appropriate use
Qualifications:
Required
- Strong SQL skills
- Solid understanding of dimensional data modeling: star schemas, fact/dimension design, grain definition, slowly changing dimensions
- Working knowledge of BI semantic model patterns (Power BI tabular models, Tableau Data Extracts, or equivalent)
- Proven ability to work directly with business users: gathering requirements, running working sessions, and iterating through feedback cycles
- Experience making structured and unstructured data useful to non-technical stakeholders
- Appreciation for why data scoping and access governance matter — especially in the context of AI systems consuming data; comfort reasoning about what a given audience should and should not see
- Familiarity with data governance concepts — lineage, access control, definitions management, documentation standards
- Iterative, collaborative working style; comfort with ambiguity and evolving requirements
Nice-to-Have
- Experience with dbt for transformation and documentation within a modern data stack
- Exposure to Snowflake platform features: dynamic tables, data sharing, row-level security, object tagging
- Knowledge of Snowflake-specific SQL syntax (window functions, semi-structured data, query optimization)
- Familiarity with Snowflake Semantic Views, Cortex Analyst, or trusted query patterns — or strong curiosity to get there quickly
- Familiarity with data catalog or data discovery tooling
- Experience in a B2B SaaS or enterprise software environment
