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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data & AI Engineer - **Company:** Accenture - **Location:** Glasgow, UK - **Salary:** £58,448.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Cloud Computing, Cloud Engineering, Continuous Delivery, Continuous Integration, Data Infrastructure, Extract Transform Load (ETL), Data Mapping, Cursor, Github, Identity and Access Management, Python (Programming Language), Machine Learning, Azure Machine Learning, SQL Databases, Data Streaming, Policy as Code, Google Cloud, Cloud Platform System, Azure Data Factory, GitHub Copilot, Snowflake, Generative AI, Git, Cloudformation, Build Management, Microsoft Fabric, Infrastructure Automation Frameworks, Bicep, AWS Data Analytics, Data Management, Virtual Agents, Terraform, Data Pipelines, Databricks - **Published:** October 7, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5916596102 ## About the Role * Cloud engineering across one or more of: Azure, AWS, GCP, Databricks, Microsoft Fabric or Snowflake * Python and SQL, with strong engineering fundamentals * Batch and/or streaming pipeline development, or cloud infrastructure and platform engineering - or both * CI/CD tooling for data and platform delivery (Git, Azure DevOps, GitHub Actions) * Practical use of GenAI or AI-assisted development tools (e.g. GitHub Copilot, Cursor, Databricks Assistant or similar) in day-to-day engineering work * Some familiarity with AI-ready architecture concepts - including awareness of how engineering decisions around quality, latency, schema, security and lineage affect the reliability of downstream AI and ML systems * A basic understanding of agentic AI concepts and how autonomous systems interact with and depend upon data and platform infrastructure You should apply if: You don't need to tick every box on this list. We're looking for curious, driven technologists who have demonstrable experience across a number of the skills and technologies above, and the appetite to grow into the rest. If you relish a challenge, embrace learning as part of your everyday work, and want to be part of a team that pushes boundaries and holds itself to a high standard - we want to hear from you. Set yourself apart with one or more of the following: * Data modelling experience including dimensional design, medallion architecture or source-to-target mapping * Hands-on experience building pipelines that support agentic workflows or LLM-based applications * Familiarity with data product principles: discoverability, reusability, quality contracts and SLAs * Hands-on experience with Decision Intelligence Platforms (e.g. Palantir, Quantexa) * Infrastructure as Code experience (Terraform, Bicep, CloudFormation or similar) * Cloud networking, identity, access management or security engineering experience * Familiarity with policy-as-code or platform governance controls * Relevant cloud or data platform certifications (e.g. Azure Data Engineer Associate, AWS Data Analytics Specialty, Databricks Certified Associate Developer, AWS Solutions Architect Associate) * AI or ML engineering certifications (e.g. Databricks Generative AI Engineer, AWS Machine Learning Specialty, Google Professional ML Engineer) ## Description As a Data & AI Engineer, you will work across the following: * Building and operating cloud data and platform solutions across modern cloud and data platforms including Azure, AWS, GCP, Databricks, Microsoft Fabric and Snowflake * Developing and maintaining scalable batch and streaming data pipelines, applying ETL/ELT patterns to ingest, validate, normalise, cleanse and transform data at scale * Building and supporting cloud platform infrastructure using Infrastructure as Code tools such as Terraform, Bicep or CloudFormation, contributing to the automated provisioning and management of cloud environments * Implementing CI/CD pipelines for data and platform solutions using Git, Azure DevOps or GitHub Actions, including AI-assisted approaches to automated testing and documentation as part of standard delivery practice * Applying data modelling principles - including dimensional/Kimball, medallion architecture and source-to-target mapping - using AI tooling to draft and iterate models, with the ability to critically review outputs for correctness and fitness for purpose * Implementing data quality, lineage, observability and monitoring capabilities, applying automation and AI-assisted tooling to build scalable, self-describing pipelines and platforms * Supporting cloud platform operations including networking, identity and access management, security fundamentals and policy adherence * Using GenAI and AI-assisted development tools to accelerate engineering delivery - writing, testing and documenting with AI assistance and applying engineering judgement to validate and correct generated outputs * Developing a working understanding of agentic concepts - including how autonomous and human-in-the-loop AI systems interact with data and platform infrastructure - to inform how you design and build solutions that are fit for agentic consumption * Collaborating with architects, product engineers and business stakeholders to understand requirements and contribute to solutions that meet both technical and business needs