At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
Forward Deployed Engineers on Finance Analytics & AI team combine deep finance domain expertise with full-stack data capabilities, a rare pairing that makes us Snowflake's most effective technical presence in the field. You embed directly with customer Finance and Analytics teams to turn Snowflake's AI platform into production systems that change how they work.
Finance is one of the first enterprise functions being modernized by AI and Snowflake is defining what that looks like. The workflows are well-defined and the legacy systems are overdue for replacement. You will deploy Cortex Agents, build semantic models, ship Streamlit apps inside Snowflake, and author AI skills that encode repeatable finance workflows into reusable tools. When you leave a customer engagement, their team can operate what you built. Success is measured in adoption, workflow impact, and customer self-sufficiency.
You also serve as Snowflake's innovation layer in the field. Product gaps, model behavior observations, and deployment patterns 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
Customer AI, Reporting & Workflow Automation (Primary Focus)
- Lead & advise end-to-end deployments of Snowflake Finance AI capabilities — Cortex Analyst, Cortex Agents, Cortex Search, CoCo (Cortex Code), and Snowflake CoWork — at strategic enterprise accounts
- Own technical scoping, design, build, and production rollout alongside customer finance, engineering, and data teams
- Embed with customer teams onsite to accelerate adoption cycles and unblock deployment blockers in real time
- Design and build AI agent workflows that encode repeatable customer business processes — revenue analysis, cost monitoring, operational reporting, procurement tracking — into reusable, invokable tools
- Translate vague customer requirements into scoped, shippable prototypes
Enablement and Knowledge Transfer
- Build the artifacts customers leave with: documented playbooks, reusable skill libraries, semantic models, and Streamlit applications their teams can maintain and extend
- Run technical workshops and working sessions to upskill customer data and analytics teams on Snowflake's AI development environment
- Author prompt structures and skill files (YAML + Markdown) that behave reliably enough that a non-technical business analyst can invoke them in plain English
- Codify deployment patterns into internal tools and playbooks that other analysts and field engineers can replicate across customer engagements
Semantic Layer and Application Development
- Build and improve semantic data models that expose customer tables to natural language queries via Cortex Analyst — turning complex schemas into something a CFO can ask a question of
- Develop production finance, operations, and analytics dashboards as Streamlit apps deployed natively inside Snowflake
- Apply rigorous evaluation standards to AI outputs before they reach customer stakeholders — you are the quality gate
Product Feedback Loop
- Influence the product roadmap with deployment reality: what actually ships in customer environments, what fails, and what unlocks adoption
- Surface field intelligence — deployment patterns, model behavior gaps, integration friction, and unmet use cases — to Snowflake's Cortex product and research teams
- Document edge cases, workarounds, and eval frameworks that make the next deployment faster
Hard Skills Required
Must-Have
- **Finance domain expertise — **You can read a balance sheet, build a variance bridge, explain ARR and NRR, and explain what drives a QoQ change in product revenue. You've worked directly with FP&A, Revenue, or Finance stakeholders
- Full-Stack Data Competency— Data Ingestion, Data Modeling, BI Reporting Automation, Analytics, to AI Orchestration
- AI-assisted development — You have used an LLM coding assistant (CoCo, Cursor, GitHub Copilot, Claude, or equivalent) as your primary development environment. You know how to write a prompt that produces production-ready output, how to steer a model heading in the wrong direction, and how to encode domain logic into a parameterized, reusable skill. Daily usage is the baseline.
- Prompt engineering and skill authoring — You can write a structured prompt or skill file (YAML + Markdown or equivalent) that routes correctly 95% of the time, handles edge cases, and encodes enough domain knowledge that the model behaves like a subject matter expert.
- Python — Modern, type-hinted, readable. You understand Python-based applications, data pipelines, and automation workflows.
- SQL — CTEs, window functions, incremental pipeline patterns.
- Client-facing communication — Your customers are Finance Leaders who think in Excel models and board decks. You write code, but your output needs to make sense to someone 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
- Snowflake Cortex — Cortex Analyst, Cortex Agents, Cortex Search, AI_SUMMARIZE, AI_EXTRACT, Dynamic Tables, semantic views
- System design under customer constraints — You scope MVPs quickly, sequence delivery, and protect timelines. You make trade-offs between speed, quality, and scope — and you communicate those trade-offs clearly to customers who are not engineers.
Soft Skills Required
- Owns the outcome, not the task That means tracking adoption after go-live, identifying stall points, and re-engaging until the customer is self-sufficient. You measure yourself in production systems that run, not in artifacts delivered.
- Able to identify & develop recurring workflows, not one-off solutions Your instinct is to codify work into a parameterized skill or playbook that other analysts can deploy at the next customer — not to build bespoke every time.
- Comfortable with ambiguity and incomplete specs You engage with customers to derive requirements. You prototype fast, show something working, gather feedback, and iterate. You come back with a working prototype and enable ownership with your customer.
- Operates with high accuracy under speed pressure Customer engagements run on compressed timelines. You scope, build, and ship a working artifact quickly. Accuracy matters more than speed — but accuracy is not a reason to be perpetually slow.
- Signal clarity for internal teams You distill messy customer deployments into clean, actionable feedback that Snowflake's product and research teams can act on. You don't just report problems — you explain root causes and suggest fixes.
Minimum Requirements
- 3–6 years of experience in finance analytics, data engineering, or a technical finance-adjacent role — with at least a portion of it customer-facing or cross-functional
- **Has used an AI coding assistant as a primary development tool **— daily usage, not occasional
- Proficient in SQL — can write window functions and complex joins without referencing documentation
- Has shipped at least one production application or analytics tool that non-technical business users (finance, ops, or sales teams) 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
- You’re engaged in at least two customer engagements — with prototypes or adoption metrics to show for it
- You've built at least one AI agent or semantic model that a customer's non-technical users can invoke in plain English
- You've shipped at least one Cortex Object or Streamlit application deployed inside a customer's Snowflake environment
- You've filed at least three product feedback items that the Cortex product team has engaged with
- Customer teams you've worked with are demonstrably faster — and can tell you exactly why
Why This Role Is Different
Most analytics and field roles stop at the recommendation. This role starts there. Specialists in this role own the build. You go onsite. You write the code. You deploy the system. You stay until it runs in production and the customer team can maintain it.
If you are fluent in both finance and Snowflake's AI development environment, you can operate at a level of customer impact that most field analytics roles don't reach. You will see more deployment patterns, more customer architectures, and more product edge cases in six months than most engineers encounter in years.
The feedback loop runs both directions: your deployments make customers faster, 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