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Data and Insight Engineer

Job in Greater London, London, Greater London, W1B, England, UK
Listing for: Orgvue
Full Time position
Listed on 2026-06-28
Job specializations:
  • IT/Tech
    Data Engineering, Data Analyst, AI Engineer (Applied/Software), Business Systems/ Tech Analyst
Salary/Wage Range or Industry Benchmark: 50000 - 70000 GBP Yearly GBP 50000.00 70000.00 YEAR
Job Description & How to Apply Below
Location: Greater London

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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