Snowflake Delivery Lead

Role Overview

The Snowflake Delivery Lead is responsible for leading the enterprise delivery of AI-powered data and analytics solutions built on Snowflake. This is a cross-functional role that combines AI product leadership, data strategy, semantic architecture, enterprise stakeholder management, delivery governance, and production readiness.

The role is accountable for ensuring that AI agents and analytics products are not only technically functional, but accurate, explainable, scalable, trusted, and aligned with enterprise decision-making needs.

The role acts as the senior bridge between business leadership, technology teams, data engineers, AI engineers, and platform partners. It requires the ability to define product direction, challenge technical assumptions, structure testing methodology, manage release risk, and provide executive-level confidence that the solution is fit for production use.

Key Responsibilities

1. Enterprise AI Data Product Leadership

• Lead the end-to-end development, validation, and deployment of AI-powered analytics products on Snowflake.
• Own the overall product direction, operating model, delivery approach, and quality bar for AI-enabled data solutions.
• Translate senior stakeholder expectations into product priorities, semantic layer requirements, agent behavior standards, test coverage, and release criteria.
• Ensure the solution supports real enterprise decision-making, not just technical experimentation.
• Define what “good” looks like across answer accuracy, explainability, consistency, usability, governance, and production resilience.
• Act as the senior product owner for AI analytics use cases, balancing business value, technical feasibility, risk, and delivery timelines.

2. AI Agent Evaluation, Testing Strategy and Quality Assurance

• Design and oversee the testing strategy for AI agents, including manual evaluation, automated regression testing, question bank design, scenario coverage, variation testing, and trace review.
• Lead the evaluation of AI outputs by checking not only final answers, but also the reasoning process, tool usage, filtering logic, assumptions, and data retrieval path.
• Distinguish between true answer mismatches, agent instability, data issues, semantic design gaps, instruction failures, and user ambiguity.
• Ensure that fixes are validated through repeat testing and variation questions before release.

3. Root Cause Diagnosis and Remediation Leadership

• Lead root cause analysis for incorrect, unstable, or regressed AI agent responses.
• Classify issues across semantic model defects, data pipeline issues, agent instruction gaps, tool-selection errors, filter logic problems, identifier resolution failures, caching issues, permissions gaps, and answer-formatting weaknesses.
• Decide whether remediation should happen in the semantic layer, agent instructions, data transformation logic, orchestration layer, application layer, or user experience.
• Guide developers on the practical implications of each fix and ensure remediation improves system reliability without introducing new regressions.
• Maintain a clear view of open defects, severity, business impact, ownership, and production-readiness implications.

4. Executive Stakeholder and Partner Management

• Serve as the interface between client leadership, business users, technology teams, Snowflake teams, engineering teams, testers, and project sponsors.
• Translate technical findings into executive-level implications, including business risk, delivery impact, production readiness, and recommended decisions.
• Manage expectations around scope, timelines, unresolved issues, deferred enhancements, and release risk.
• Build trust with stakeholders by providing clear judgment on what is ready, what is risky, what can be deferred, and what must be fixed before production.

5. Production Readiness, Release Governance and Risk Management

• Define go / no-go criteria, acceptance thresholds, UAT scope, deployment sequencing, rollback considerations, and post-deployment validation.
• Coordinate across workstreams to ensure semantic views, Snowflake pipelines, Qlik dependencies, Airflow DAGs, permissions, agent instructions, and testing evidence are aligned before release.
• Ensure governance approvals are supported by clear documentation, testing evidence, risk assessment, and mitigation plans.
• Make pragmatic decisions on whether enhancements should be included in release scope or deferred to future iterations.
• Support post-production monitoring by identifying key scenarios, known risks, and validation checks after deployment.

Required Skills and Experience

• Extensive experience in enterprise data, analytics, AI product delivery, business intelligence, or digital transformation.
• Strong understanding of Snowflake, semantic layers, data modeling, SQL-based business logic, enterprise reporting, and analytics workflows.
• Experience leading complex AI, data, or analytics delivery programmes involving multiple stakeholders and production release accountability.
• Strong ability to evaluate AI-generated answers for correctness, explainability, consistency, and business alignment.
• Experience designing testing methodologies for AI agents, text-to-SQL systems, semantic models, or analytics platforms.
• Ability to conduct root cause analysis across business logic, semantic layers, data pipelines, AI instructions, and application behavior.
• Strong executive communication skills, with the ability to explain technical risks and trade-offs in commercial and operational terms.
• Experience managing senior stakeholders, delivery teams, vendors, platform partners, and governance forums.
• Ability to operate effectively in ambiguous, high-pressure enterprise environments with tight timelines and high expectations.

Preferred Experience

• Experience with Snowflake Cortex, LLM-powered analytics, text-to-SQL, semantic search, AI agents, or natural language business intelligence.
• Experience with Qlik, Tableau, Power BI, or other enterprise BI platforms.
• Experience in financial analytics, investment reporting, portfolio analytics, enterprise management reporting, or regulated enterprise environments.
• Familiarity with data orchestration tools such as Airflow.
• Experience leading UAT, production deployments, change control processes, and post-release validation.
• Experience building reusable frameworks for AI evaluation, regression testing, semantic governance, and enterprise AI product quality.