• Own the technical architecture and roadmap for our most complex Analytics Engineering initiatives - including semantic layer design, source-of-truth consolidation, and the data foundation for AI and agent-based use cases • Architect Forward's semantic layer and metrics standards so key business KPIs are defined once, governed clearly, and consumed consistently across dashboards, models, AI agents, and downstream products • Lead the technical design of the AI-ready data platform - making the modeling, metadata, and governance decisions that make Snowflake Intelligence and other AI/agent capabilities trustworthy, performant, and production-ready • Drive technical excellence across our dbt project: model architecture, materialization and incremental strategies, performance tuning, macros, testing patterns, and CI/CD practices that scale as data volume and team size grow • Set and uphold a high bar for craftsmanship across the team - defining standards for SQL style, modeling patterns, documentation, and data quality, and modeling those standards in your own work • Mentor Senior and Analytics Engineers through hands-on code review, pairing, and design feedback - accelerating their growth into stronger technical contributors • Partner with the Manager of Analytics Engineering on technical strategy, hiring, and roadmap planning - acting as a deputy for technical decisions and unblocking the team on the hardest problems • Lead deep technical partnerships with Data Science, Data Engineering, and Core Technology - owning schema migrations, feature deployments, and streaming pipeline contributions where Analytics Engineering is on the critical path • Evaluate and operationalize high-value third-party data sources and emerging tooling (e.g., Snowflake Cortex, semantic layer frameworks, observability tools) and make recommendations that elevate the platform • Champion data governance and quality at the platform level - including dbt tests, lineage, cataloging, observability, and compliance with security and regulatory standards - so both stakeholders and AI systems can trust the numbers

• Own the technical architecture and roadmap for our most complex Analytics Engineering initiatives - including semantic layer design, source-of-truth consolidation, and the data foundation for AI and agent-based use cases • Architect Forward's semantic layer and metrics standards so key business KPIs are defined once, governed clearly, and consumed consistently across dashboards, models, AI agents, and downstream products • Lead the technical design of the AI-ready data platform - making the modeling, metadata, and governance decisions that make Snowflake Intelligence and other AI/agent capabilities trustworthy, performant, and production-ready • Drive technical excellence across our dbt project: model architecture, materialization and incremental strategies, performance tuning, macros, testing patterns, and CI/CD practices that scale as data volume and team size grow • Set and uphold a high bar for craftsmanship across the team - defining standards for SQL style, modeling patterns, documentation, and data quality, and modeling those standards in your own work • Mentor Senior and Analytics Engineers through hands-on code review, pairing, and design feedback - accelerating their growth into stronger technical contributors • Partner with the Manager of Analytics Engineering on technical strategy, hiring, and roadmap planning - acting as a deputy for technical decisions and unblocking the team on the hardest problems • Lead deep technical partnerships with Data Science, Data Engineering, and Core Technology - owning schema migrations, feature deployments, and streaming pipeline contributions where Analytics Engineering is on the critical path • Evaluate and operationalize high-value third-party data sources and emerging tooling (e.g., Snowflake Cortex, semantic layer frameworks, observability tools) and make recommendations that elevate the platform • Champion data governance and quality at the platform level - including dbt tests, lineage, cataloging, observability, and compliance with security and regulatory standards - so both stakeholders and AI systems can trust the numbers

• Convey the strategic advantages and technical application of Snowflake's Analytics capabilities, address client inquiries, and conduct follow-up engagements to facilitate sales progression. • Articulate Snowflake's Analytics functionalities and provide a comparative analysis with competitor offerings for prospective clients. • Ascertain client-defined success metrics for the adoption and utilization of Snowflake's Analytics features. • Recognize, address, and reconcile discrepancies between Snowflake's methodologies and client-specific requisites concerning Analytics features. • Provide comprehensive resolutions to client objections and concerns. • Develop and deliver bespoke demonstrations that highlight Snowflake's value proposition, tailored to address specific client requirements pertaining to analytic functionalities. • Demonstrate the ability to elucidate Snowflake's security, privacy, and governance considerations with respect to Analytics. • Be the technical expert in the room that positions Snowflake’s analytical features and value to technical stakeholders at Snowflake’s customers across the Americas. • Function as the principal technical authority, articulating Snowflake's analytical functionalities and value proposition to technical stakeholders within customer organizations across the Americas. • Collaborate with Snowflake account teams and customer advocates to define and execute Proof of Concepts, ensuring successful outcomes and demonstrable technical achievements that validate Snowflake's capabilities, including comprehensive executive summaries and business value assessments. • Develop and disseminate content to facilitate team and organizational growth, such as blog articles, conference presentations, and technical materials including notebooks and demonstrations. • Act as the Voice of Customer by triaging Product gaps and communicating their priority to Product Management. • Develop sales programs with Product Marketing around GTM for Analytics features.

• Apply hands on analytics and data expertise to solve complex, fast moving financial and operational problems in the health tech space • Design, develop and maintain scalable, analytics ready data models (primarily in dbt) that power a PBM and care navigation data ecosystem that support client reporting, performance analytics, operational analysis and self-service business intelligence. • Translate complex pharmacy benefits and healthcare navigation workflows, business rules and operational processes into transparent, maintainable, and auditable data models. • Partner closely with data and analytics engineers, data analysts and business stakeholders to deliver reliable data products that can be leveraged across multiple use cases and continuously optimize data models and warehouse performance to support large-scale PBM/care navigation datasets and growing business needs. • Implement automated data quality checks, testing frameworks and reconciliation processes to ensure data reliability. • Establish documentation standards, lineage and analytics engineering guardrails that promote transparency and auditability. • Contribute to engineering best practices including version control, CI/CD, incremental models, code reviews, and observability. • Contribute to data governance by establishing modeling standards, documentation and guardrails that support auditability, explainability and long term maintainability. • Build data foundations that enable future agentic analytics engineering use cases, including self-service analytics and AI-assisted insight generation. • Stay informed on emerging technologies and identify opportunities to incorporate AI into analytics engineering workflows.