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# Data Engineering Manager

- **Company:** Accenture
- **Location:** Glasgow, UK
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Mapping, DataOps, Azure Machine Learning, Google Cloud, Azure Data Factory, Snowflake, Generative AI, Agentic-AI, Microsoft Fabric, AI Platforms, AWS Data Analytics, Data Management, Claude, Virtual Agents, Drift Detection, Data Pipelines, Automation Anywhere, Human in the Loop, Databricks
- **Published:** October 6, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ce90b8863a582952

## About the Role

* Design and delivery of cloud-scale data engineering solutions across one or more of: Azure, Databricks, Microsoft Fabric, Snowflake, AWS or GCP
 * AI-assisted pipeline development - proven experience using GenAI tooling to accelerate engineering delivery and governing the quality of outputs
 * Data quality, observability and automated governance - including schema drift detection, lineage management and AI-driven quality controls
 * Data modelling including dimensional/Kimball design, medallion architecture and source-to-target mapping, with the ability to translate these into scalable, reusable patterns
 * ETL/ELT delivery and legacy-to-cloud migration at scale
 * Data product thinking - experience owning the full pipeline-to-product journey, with a consumption-first mindset and understanding of data product principles
 * Agentic AI patterns in a data engineering context - experience designing or delivering pipelines that incorporate autonomous or human-in-the-loop AI workflows
 * CI/CD and engineering standards for data platforms, including test automation and AI-assisted delivery practices
 * Experience leading and mentoring engineering teams, setting technical direction and driving delivery to a high standard, You don't need to tick every box on this list. We're looking for experienced data engineering leaders who have demonstrable depth across a number of these areas, and the technical curiosity to grow into the rest. If you set high standards, lead teams with purpose, embrace AI as a fundamental shift in how data engineering is practised - and want to work on some of the most ambitious data and AI programmes in the market - we want to hear from you., * Hands-on experience designing or delivering agentic data pipeline capabilities or autonomous data operations in production
 * Experience building reusable data product frameworks, templates or accelerators
 * Demonstrable expertise with Decision Intelligence Platforms (e.g. Palantir, Quantexa)
 * Experience contributing to commercial propositions, bids or client-facing accelerators
 * Data modelling experience including dimensional design, medallion architecture or source-to-target mapping
 * Familiarity with data product principles: discoverability, reusability, quality contracts and SLAs
 * Relevant cloud or data platform certifications (e.g. Azure Data Engineer Associate, Databricks Certified Professional Data Engineer, AWS Data Analytics Specialty)
 * AI platform and engineering certifications (e.g. Databricks Generative AI Engineer, AWS Machine Learning Specialty, Google Professional ML Engineer)
 * AI practitioner certifications demonstrating hands-on fluency with leading AI platforms - for example Anthropic Claude Certified Architect (CCAR-F or CCAR-P), Microsoft Agentic AI Business Solutions Architect (AB-100) or AI Agent Builder Associate (AB-620), or equivalent credentials from other leading AI vendors

## Description

* Lead delivery of cloud-scale data engineering programmes and platform migration initiatives across Azure, Databricks, Microsoft Fabric, Snowflake, AWS and GCP
 * Drive AI-assisted pipeline development as standard engineering practice - setting team norms around the use of GenAI tooling for pipeline build, testing and documentation, and governing the quality of AI-generated outputs
 * Design and implement data quality, schema drift detection and lineage capabilities using AI-driven automation - moving teams away from manual quality gates towards intelligent, self-describing pipelines
 * Own data product design and quality end to end - from consumption requirements through data modelling, pipeline delivery and ongoing governance - ensuring what the team builds is fit for business and AI consumption, not just technically correct
 * Lead ETL/ELT modernisation and cloud migration programmes, applying AI-assisted approaches to accelerate analysis, mapping and delivery of legacy-to-cloud transitions
 * Architect observability, resiliency and self-healing pipeline capabilities - using AI-driven monitoring and alerting to anticipate issues and reduce operational toil
 * Drive adoption of agentic pipeline patterns - designing data workflows that incorporate autonomous and human-in-the-loop AI operations, and leading teams in understanding how to build, govern and iterate on these effectively
 * Translate data modelling and mapping patterns into reusable agent skills and automation frameworks - moving the team beyond one-off delivery towards scalable, repeatable capability
 * Establish CI/CD, testing automation and engineering standards across data engineering delivery, incorporating AI-assisted approaches as a baseline expectation
 * Mentor and develop engineering teams in AI-augmented data engineering practices, setting technical standards and fostering a culture of continuous improvement
 * Contribute to proposition development, accelerators and reusable data engineering assets that strengthen our market offering and reduce delivery time on future engagements