Associate Data Architect

About the role

Sr. Data Architect with 10–15 years of experience. Location: New York, NY. We are seeking an experienced Snowflake Architect with strong Snowflake Cortex AI and agentic-solution experience to lead the architecture and delivery of governed Finance data and AI capabilities. The role will connect Finance business meaning, semantic models and Snowflake data with Cortex Skills, agents, reusable tools and Talk-to-Data experiences. The architect will define scalable solution patterns, guide engineering teams and establish the quality, security, lifecycle and operational controls required for production adoption. Success measures include ensuring Finance AI use cases are grounded on governed Snowflake data with approved semantic definitions and appropriate access controls; Cortex Skills and agents meeting agreed thresholds for accuracy, confidence, traceability, security and operational readiness; reusable architecture and calculation patterns reducing one‑off implementation and improving consistency; engineering teams receiving clear, implementable designs and timely architectural decisions; and solutions meeting agreed performance, reliability, data‑quality, auditability and production‑support expectations.

Responsibilities

• Define the end-to-end architecture for Snowflake-based Finance data, semantic and AI solutions across reporting, analytics and conversational use cases.
• Design Cortex Skills, Cortex Agents, reusable agent tools, skill packages and governed Talk-to-Data capabilities.
• Architect how AI agents consume approved semantic models, verified queries, metadata, business definitions and Finance context.
• Define logical, dimensional, physical and semantic modelling patterns for Finance domains such as General Ledger, P&L, Revenue and Spend.
• Create reusable design patterns for MTD, QTD, YTD and other dynamic Finance calculations, including the appropriate use of Snowflake Dynamic Tables.
• Guide the implementation of Snowflake data-consumption layers, transformation patterns, data services and APIs supporting dashboards and AI experiences.
• Establish evaluation frameworks using golden question-and-answer sets, regression testing, confidence scoring, abstention and user-feedback loops.
• Define lifecycle controls for semantic assets, Cortex Skills, agents, prompts, tools, metadata and verified queries.
• Embed identity, entitlements, data protection, auditability, prompt-injection controls and responsible-AI requirements into the solution design.
• Define observability for quality, accuracy, latency, usage, drift, failures and operational performance, together with recovery workflows and runbooks.
• Partner with Finance SMEs, Product Owners, data owners, governance teams, security reviewers and engineers to translate business needs into an approved architecture and delivery backlog.