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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI-Enabled Data Analytics Engineer [212690] - **Company:** Aquent - **Location:** London, UK - **Salary:** £91,437.0 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Business Analytics Applications, Data Analysis, Code Review, Information Engineering, Extract Transform Load (ETL), Data Transformation, Cursor (Graphical User Interface Elements), Software Debugging, Programming Tools, Github, Python (Programming Language), Performance Tuning, Markdown, Software Engineering, SQL Databases, Visual Studio Online, GitHub Copilot, Prompt Engineering, Git, Pyspark, Data Analytics, Data Management, Virtual Agents, GPT, Data Pipelines - **Published:** September 1, 2026 - **Apply:** https://www.totaljobs.com/job/data-analytics-engineer/aquent-job107923005 ## About the Role * Experience in Data Analytics, Analytics Engineering, or Data Engineering. * Strong experience with leading data analytics platforms, SQL, and Python. * Hands-on experience using AI-assisted development tools such as Claude Code, Cursor, VS Code, GitHub Copilot, or ChatGPT to support coding, debugging, documentation, testing, or analysis workflows. * Strong analytical thinking, communication, and problem-solving skills. * Ability to review, validate, and improve AI-generated code, documentation, and analytical outputs before they are used in business or production contexts. Technical Skills * Programming: Python, SQL, PySpark * Data Platforms: Leading data analytics platforms (e.g., data lakehouses) * Data Engineering: ETL/ELT, Data Modeling, Data Quality, Performance Optimization * AI Development: Agent workflows, prompt engineering, context engineering, validation patterns * Development Tools: Git, GitHub Core Competencies * Critical thinking and sound engineering judgment. * Ability to validate, challenge, and own AI-generated outputs. * Strong understanding of business processes and stakeholder needs. * Ability to translate ambiguous business needs into well-scoped analytics, automation, and AI-agent solutions. * Ownership, accountability, and a continuous improvement mindset. * Ability to identify opportunities to automate business processes using AI while ensuring governance, quality, and trust. This role is ideal for a hands-on engineer who can combine data engineering, analytics, software development, and responsible AI adoption to deliver reliable, scalable business solutions. ## Description * Design, build, and optimize data pipelines and analytics solutions using leading data analytics platforms. * Develop efficient SQL and Python solutions for data transformation, analysis, and automation. * Utilize AI-assisted development tools, including Claude Code, Cursor, VS Code, GitHub Copilot, and ChatGPT, to accelerate development, debugging, documentation, testing, and code review while maintaining full accountability for the quality and correctness of the final output. * Build and improve AI agents, reusable skills, prompt templates, and automation workflows that support repeatable analytics, documentation, testing, and business-process automation use cases. * Own AI agent documentation, context files, Markdown-based knowledge sources, prompt libraries, and business rules that govern AI behavior and make outputs repeatable, auditable, and maintainable. * Apply context engineering by curating source materials, instructions, examples, business rules, and validation criteria that improve the quality, consistency, and reliability of AI-generated outputs. * Validate AI-generated code, analyses, and recommendations to ensure technical accuracy and business relevance. * Partner with business stakeholders to translate requirements into scalable analytics and AI solutions. * Ensure data quality, governance, and continuous improvement of analytics and AI capabilities. ## Related Videos - [Livecoding with AI](https://www.wearedevelopers.com/videos/1201-livecoding-with-ai) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [The AI-Ready Stack: Rethinking the Engineering Org of the Future](https://www.wearedevelopers.com/videos/1706-the-ai-ready-stack-rethinking-the-engineering-org-of-the-future) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [From Syntax to Singularity: AI’s Impact on Developer Roles](https://www.wearedevelopers.com/videos/900-from-syntax-to-singularity-ai-s-impact-on-developer-roles) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Transforming Software Development: The Role of AI and Developer Tools](https://www.wearedevelopers.com/magazine/527-transforming-software-development-the-role-of-ai-and-developer-tools) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Top AI Tools for Developers in 2025](https://www.wearedevelopers.com/magazine/560-top-ai-tools-for-developers-in-2025) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)