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Accenture

Advises businesses and runs their technology

Data Engineering Manager

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeGlasgow, UKNo salary listed

About the Job

Requirements

  • Significant experience designing and delivering cloud-scale data engineering solutions across one or more of Azure, Databricks, Microsoft Fabric, Snowflake, AWS, or GCP.
  • Experience using generative AI tooling to accelerate pipeline development and governing the quality of AI-generated outputs.
  • Experience with data quality, observability, and automated governance, including schema drift detection, lineage management, and AI-driven quality controls.
  • Data modelling experience, including dimensional or Kimball design, medallion architecture, and source-to-target mapping, with the ability to translate these into scalable, reusable patterns.
  • Experience delivering ETL/ELT and legacy-to-cloud migrations at scale.
  • Experience owning the pipeline-to-product journey, with a consumption-first mindset and understanding of data product principles.
  • Experience designing or delivering data engineering pipelines that incorporate autonomous or human-in-the-loop AI workflows.
  • Experience with 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.

Responsibilities

  • Lead delivery of cloud-scale data engineering programmes and platform migration initiatives across Azure, Databricks, Microsoft Fabric, Snowflake, AWS, and GCP.
  • Set team norms for using generative AI tools in pipeline development, testing, and documentation, and govern the quality of AI-generated outputs.
  • Design and implement data quality, schema drift detection, and lineage capabilities using AI-driven automation.
  • Own data product design and quality end to end, from consumption requirements through data modelling, pipeline delivery, and ongoing governance, ensuring outputs are fit for business and AI consumption.
  • Lead ETL/ELT modernisation and cloud migration programmes, applying AI-assisted approaches to legacy-to-cloud analysis, mapping, and delivery.
  • 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 by designing data workflows with autonomous and human-in-the-loop AI operations, and lead teams in building, governing, and iterating on them.
  • Translate data modelling and mapping patterns into reusable agent skills and automation frameworks.
  • Establish CI/CD, testing automation, and engineering standards across data engineering delivery, incorporating AI-assisted approaches.
  • Mentor and develop engineering teams in AI-augmented data engineering practices, set technical standards, and foster continuous improvement.
  • Contribute to proposition development, accelerators, and reusable data engineering assets to strengthen the market offering and reduce delivery time on future engagements.

Desired Qualifications

  • 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.
  • Expertise with Decision Intelligence Platforms such as Palantir or 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 including discoverability, reusability, quality contracts, and service-level agreements.
  • Relevant cloud or data platform certifications, such as Azure Data Engineer Associate, Databricks Certified Professional Data Engineer, or AWS Data Analytics Specialty.
  • AI platform and engineering certifications, such as Databricks Generative AI Engineer, AWS Machine Learning Specialty, or Google Professional Machine Learning Engineer.
  • AI practitioner certifications demonstrating hands-on fluency with leading AI platforms, such as Anthropic Claude Certified Architect, Microsoft Agentic AI Business Solutions Architect, or AI Agent Builder Associate, or equivalent credentials from other leading AI vendors.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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