Senior Data Engineer Agentic AI Enablement & Snowflake Data Platform

Role Overview

We are seeking a highly skilled Senior Data Engineer to support enterprise AI and analytics initiatives. This role is responsible for designing, developing, and maintaining the data foundation that powers advanced AI-driven applications, including agentic AI solutions.

The ideal candidate is not just a traditional data engineer, but someone who can bridge data engineering, data quality, semantic modelling, and AI enablement. They will create scalable Snowflake data products, optimize complex SQL workloads, design curated semantic views, and develop metadata and skill definitions that enable AI agents to generate accurate, explainable, and business-relevant insights.

This role is critical to enabling intelligent, data-driven solutions across multiple business domains and enterprise data sources.

Key Responsibilities

Data Engineering & Snowflake Development

• Design, build, and maintain scalable Snowflake data models, views, dynamic tables, and data pipelines.

• Develop optimized, reusable, and business-friendly semantic layers for AI and analytics applications.

• Create curated datasets and agent-ready views that support natural language querying and AI-driven insights.

• Integrate data from multiple enterprise source systems across business and operational domains.

• Ensure high-performance query execution through advanced SQL optimization and Snowflake best practices.

AI Agent Skill & Semantic Layer Development

• Develop metadata-driven skill definitions and markup files that enable AI agents to understand business context and data relationships.

• Translate business requirements into reusable AI agent skills and routing logic.

• Design semantic models that support agentic AI applications, dashboards, reporting, analytics, and data science workflows.

• Partner with AI engineers to improve agent accuracy, grounding, and reasoning capabilities.

Data Quality & Governance

• Define and implement enterprise data quality frameworks.

• Build automated validation checks covering completeness, accuracy, consistency, timeliness, referential integrity, and data drift detection.

• Proactively monitor upstream and downstream data issues.

• Establish trusted business definitions and standardized KPI calculations across data products.

Performance Optimization

• Analyze large-scale query workloads and optimize performance.

• Configure and improve materialized views, dynamic tables, clustering strategies, query execution plans, and warehouse utilization.

• Ensure solutions scale efficiently across multiple business units and data domains.

Business Partnership

• Work closely with business subject matter experts, data scientists, AI engineers, and product owners.

• Convert business questions into robust data products and AI use cases.

• Support the deployment and onboarding of new business areas and data sources.

Required Qualifications

Experience

• 5+ years of data engineering experience.

• 3+ years of hands-on Snowflake development experience.

• Experience supporting enterprise analytics, AI, or data product initiatives.

Technical Skills

• Snowflake: Strong experience with Snowflake architecture, dynamic tables, materialized views, streams and tasks, secure views, schema design, query optimization, performance tuning, and warehouse optimization.

• SQL: Expert-level skills including complex joins, window functions, common table expressions (CTEs), analytical functions, query profiling, data transformations, and performance tuning. Comfortable working with datasets exceeding hundreds of millions of records.

• Data Modelling: Experience with dimensional modelling, semantic layers, data products, curated business views, KPI standardization, and metadata management.

• Data Quality: Hands-on experience implementing data validation frameworks, automated monitoring, data reconciliation, data observability, and root cause analysis.

AI & Agentic Technologies (Preferred)

Experience in one or more of the following:

• Agent-based systems

• Knowledge graphs

• Retrieval systems and semantic search

• Large Language Models (LLMs) and prompt engineering

• Agent skill development

• Metadata-driven AI applications

Preferred Experience

• Experience working with large-scale enterprise data platforms and diverse source systems.

• Experience supporting AI-powered analytical applications.

• Familiarity with process improvement or operational excellence methodologies (e.g., Lean, Six Sigma).