Data Engineering Manager
Location: Glasgow
Задачи
•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 the market offering and reduce delivery time on future engagements
О команде
Accenture's Data & AI team combines deep technology, business and industry expertise to design and deliver large, high-profile data and AI solutions across Financial Services, Resources, Products, Communications and Health & Public Services. The practice is built around three strategic capabilities: AI Platform Engineering, AI Pipelines, and Data Products. As a Data Engineering Manager, you will lead delivery of AI-powered data engineering programmes — owning the full journey from pipeline design and build through to the data products that serve clients' AI, analytics and business needs. The team operates in an AI-augmented delivery model where AI-assisted pipeline development, automated data quality and agentic data operations are current practice, and managers are expected to lead teams in adopting and embedding these approaches as standard.
Требования
•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 — candidates with demonstrable depth across a number of these areas, plus technical curiosity to grow into the rest, are encouraged to apply.
Set yourself apart
•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
Условия
•Competitive basic salary plus an extensive benefits package: 30 days' vacation per year, private medical insurance, and 3 extra days' leave per year for charitable work of your choice
•Flexibility and mobility are required to deliver this role, as there will be requirements to spend time onsite with clients and partners
•Closing Date for Applications: 22/01/27 (Accenture reserves the right to close the role prior to this date should a suitable applicant be found)
