About the Role

Forward Deployed Analytics Engineers combine domain expertise with full-stack data and analytics engineering capabilities, a rare pairing that makes us Snowflake's most effective technical presence in the field. You embed directly with customer data, analytics, and business teams to build the data foundations that power Snowflake's AI platform.

This role is focused on the layers that make AI reliable: clean, well-modeled data, governed pipelines, and semantic models that expose business meaning to natural language interfaces. You will design rigorous data models, build and instrument pipelines, and construct the semantic layer that sits between raw data and AI agents. When you leave a customer engagement, their data is structured, trusted, and agent-ready. The deployment patterns and product gaps you surface feed directly back to Cortex product and research teams, making you both a practitioner and a source of signal for what gets built next.
What You'll Work On Data Modeling and Architecture

• Architect flexible, performant data models that drive customers toward single sources of truth across their key business domains
• Use SQL, Python, dbt, and Snowflake to build and maintain data infrastructure for reporting, analysis, and automation
• Perform data QA and develop automated testing procedures for Snowflake data models
• Provide input into data governance strategies including permissions, data lineage, and data definitions

Semantic Layer and Agent Readiness

• Build semantic data models that expose customer tables to natural language queries via Cortex Analyst, turning complex schemas into something a business stakeholder can ask a question of
• Define and validate the metrics, dimensions, and relationships that AI agents need to reason correctly over customer data
• Identify and resolve gaps in data structure, naming, and coverage that would cause an agent to fail or produce incorrect results

Enablement and Knowledge Transfer

• Build the artifacts customers leave with: documented playbooks, reusable data model templates, and semantic model libraries their teams can maintain and extend
• Run technical workshops to upskill customer data and analytics teams on Snowflake's AI development environment
• Author semantic view configurations and skill files (YAML + Markdown) that a non-technical analyst can invoke in plain English

Hard Skills Required Must-Have

• Advanced SQL: CTEs, window functions, incremental pipeline patterns. You can write complex queries without referencing documentation.
• Analytics engineering and data modeling: Experience building data infrastructure involving large-scale relational datasets; strong instincts for pipeline design, QA, and testing across the full stack from ingestion through semantic layer.
• Python: Modern, type-hinted, readable. You understand Python-based data pipelines and automation workflows.
• AI-assisted development: You have used an LLM coding assistant (CoCo, Cursor, GitHub Copilot, Claude, or equivalent) as your primary development environment. Daily usage is the baseline.
• Semantic modeling: You can write a semantic view configuration or structured skill file that handles edge cases and encodes enough domain knowledge that the model behaves like a subject matter expert.
• Client-facing communication: You write code, but your output needs to make sense to a business leader who has never opened a terminal. You are the translation layer between what Snowflake's AI can do and what the customer actually needs.

Strong Plus

• dbt: Experience building and maintaining dbt projects with testing, documentation, and CI/CD pipelines.
• Snowflake Cortex: Cortex Analyst, Cortex Agents, Cortex Search, semantic views, Dynamic Tables.
• Experience with Airflow or other orchestration frameworks.
• Familiarity with enterprise business systems (ERP, CRM, HRIS, or similar).

Soft Skills Required

• Owns the outcome: Tracks adoption after go-live, identifies stall points, and re-engages until the customer's data is reliable and their team can maintain it independently.
• Codifies, doesn't customize: Instinct is to turn patterns into reusable templates and playbooks that the next engineer can deploy at the next customer, not to build bespoke every time.
• Comfortable with ambiguity: Engages with customers to derive requirements, prototypes fast, gathers feedback, and iterates.
• Signal clarity: Distills messy customer deployments into clean, actionable feedback for Snowflake's product and research teams, explaining root causes and suggesting fixes, not just reporting problems.

Minimum Requirements

• 5+ years of experience in analytics engineering, data engineering, or a related technical role, with at least a portion of it customer-facing or cross-functional
• Daily use of an AI coding assistant as a primary development tool
• Proficient in SQL; can write window functions and complex joins without referencing documentation
• Experience with dbt or equivalent data modeling framework
• Has shipped at least one production data model or pipeline that non-technical business users actually relied on
• Comfortable in Git (PRs, branches, code review)
• Demonstrable experience translating business requirements into technical specifications

What Success Looks Like at 90 Days

• Engaged in at least two customer engagements, with measurable data quality or semantic layer improvements to show for it
• Built at least one semantic model that a customer's non-technical users can query in plain English via Cortex Analyst
• Identified and resolved at least one upstream data quality or modeling issue that was blocking an AI use case
• Filed at least three product feedback items that the Cortex product team has engaged with

Why This Role Is Different

Most analytics engineering roles stop at the data model. Most field roles stop at the recommendation. This role starts where both leave off. You own the full data stack from source ingestion to semantic layer, and you ensure every layer is clean, tested, and structured for AI agents to reason over reliably. You go onsite. You write the code. You build the semantic foundation. You stay until it runs in production and the customer team can maintain it.

If you are fluent in analytics engineering and Snowflake's AI development environment, you can operate at a level of customer impact that most field or internal analytics roles don't reach. Your work makes customers' data agent-ready, and your field observations make Snowflake's AI platform better.

Snowflake is growing fast, and we're scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.

How do you want to make your impact?

For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com