Data and Insight Engineer

Requirements

• Strong SQL and data modelling expertise
• Experience working with Snowflake
• Experience with analytics engineering tools such as dbt
• Proficiency with Python or similar languages for data workflows
• Experience building and maintaining data pipelines
• Experience translating business questions into analytical models and metrics
• Experience working with analysts, product teams, and business stakeholders to support decision-making
• (Desirable) Experience working with large language models (LLMs) or AI-enabled analytics platforms
• (Desirable) Familiarity with prompt design or AI-assisted analytical workflows
• (Desirable) Familiarity with Snowflake Intelligence

What the job involves

• The Data & Insight Engineer will build and own an AI-native analytics environment where insights are generated automatically from data rather than manually through dashboards and reports
• This role combines data engineering, analytics engineering, and AI-enabled insight generation. Responsibilities include building semantic data models, developing automated insight pipelines, and integrating Snowflake Cortex capabilities to support conversational analytics and AI-driven business intelligence
• Data Pipeline Engineering
• Build and maintain robust data ingestion and transformation pipelines
• Integrate data from operational systems into the analytics platform
• Maintain data quality frameworks and validation checks
• Optimise performance of data processing and analytics workloads
• AI Insight Pipeline Development and ownership
• Automate recurring analysis traditionally performed manually
• Enable natural-language analytics across curated datasets
• Develop systems that translate business questions into structured data queries
• Semantic Data Modelling
• Design and maintain curated business data models that support reliable analytics and AI-driven insights
• Define core business entities, metrics, and KPI definitions
• Build and maintain semantic layers within Snowflake
• Governance and Quality Assurance
• Monitor model performance, accuracy, and cost usage
• Implement safeguards to ensure reliable and explainable outputs
• Maintain governance standards for AI-enabled analytics workflows

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