Forward Deployed Analytics Engineer, Snowflake
AI Specialist
$156-204.7k
Additionally, this role is eligible to participate in Snowflake’s bonus and equity plan
More information about location
3-5 days a week in office (Menlo Park, CA)
AI Data Cloud for data sharing and app building
Job no longer available
AI Data Cloud for data sharing and app building
1001+ employees
Job no longer available
$156-204.7k
Additionally, this role is eligible to participate in Snowflake’s bonus and equity plan
More information about location
3-5 days a week in office (Menlo Park, CA)
1001+ employees
Company mission
On a mission to mobilize the world's data.
Role
Who you are
- 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)
- 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
- 8+ 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 the job involves
- 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
- Location: Role based out of our Menlo Park Office + 50% travel both in US & around the World
- 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
- 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
Our take
Developed as a fresh alternative to the archaic systems of data governance and information silos, Snowflake is a frictionless cloud data platform that removes the barriers preventing organisations of all sizes from unleashing the true value of their data.
The company equips organizations with a single, integrated platform that offers the only data warehouse built for the cloud; instant, secure and governed access to their entire network of data; and a core architecture to enable many types of data workloads, including a single platform for developing modern data applications.
Major competitors include Azure, SQL and AWS, and Snowflake has managed to keep ahead of the competition, cementing itself as a global force. Across its 1300+ partners, the company's cloud manages over 250PB of data (with more than 515 million data workloads running every day). Its continued focus on product R&D and partnership expansion have Snowflake well-positioned to manage itself amidst the competition.
Freddie
Company Specialist at Welcome to the Jungle
Insights
37% employee growth in 12 months
Company
Funding (last 2 of 9 rounds)
Feb 2020
$479m
SERIES G
Oct 2018
$450m
LATE VC
Total funding: $1.4bn
Company benefits
- Comprehensive health insurance plans
- Health savings accounts
- Robust retirement plans
- Life and disability insurance
- Weekly online lunch and learns
- Virtual workout classes
- Ergonomic work-from-home equipment
- On-demand mental health and wellness programs
- Fertility benefits and family planning resources
- Generous time-off and various leave plans
- Onsite and Remote Work
- Employee discounts and pre-tax selections
- New hire equity + Employee Stock Purchase Plan (ESPP)
- Quarterly bonus or commission program
Company HQ
Menlo Park, CA
Leadership
Benoit Dageville
(Co-Founder & President of Product)Benoit, a leading expert in parallel execution and self-tuning databases, founded Snowflake after 10+ years at Oracle as a lead architect. He holds a PhD in Computer Science and has 270+ patents.
Thierry Cruanes
(Co-Founder)Thierry is a leading expert in query optimization and parallel execution. He has a PhD in Computer Science with a focus in Database Systems and is a named inventor on 200+ patents.
Sridhar Ramaswamy
(Chief Executive Officer)Sridhar joined Snowflake in 2023 via the Neeva acquisition as SVP of AI and later became CEO. He co-founded Neeva and previously led Google’s Ads business, growing it from $1.5B to $100B.
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