Data Specialist - Data Analytics Engineer
Summary
Data Analytics Engineer at Morrisons who designs and owns data models and a central semantic layer on Google Cloud, bridging data pipelines and business insight for retail and supply chain analytics. Day to day: advanced SQL, Dataform/Git CI/CD transformations, LookML semantic layer, data quality checks, documentation, and mentoring.
In this role, you will shape the data foundation that informs decision-making across Morrisons. You’ll design and manage data models and a central semantic layer on Google Cloud, bridging pipelines and business insight. You’ll collaborate with analysts and stakeholders to ensure reliable, well-documented data for reporting and analytics. Expect a hands-on role that blends data architecture, governance, and delivery in a fast-paced retail environment.
Pay / Benefits- 15% discount in store
- Annual bonus scheme
- Career progression and development opportunities
- Generous holiday entitlement
- Market leading pension scheme and life assurance
- Private healthcare
- Translate business needs into robust data architecture and optimised analytical datasets
- Own data model delivery for the domain, with accurate documentation and definitions
- Champion data literacy and standard transformation guidelines across the organisation
- Mentor trainees on SQL standards, data testing, version control, and domain context
- Facilitate communication between engineering teams and non-technical stakeholders
- Build reusable conformed data layers to reduce processing waste and cloud costs
- Design, test, and deploy secure data models and transformations in Google Cloud Platform
- Maintain the centralised semantic layer (e.g. LookML or similar) for consistent definitions across reporting
- Map enterprise ontologies to relational models reflecting retail and supply chain processes
- Implement metadata repositories, data dictionaries, and lineage tools
- Define test conditions and run automated data quality checks
- Refine models to adapt to changes in operations and priorities
- Data Modelling with recognised patterns (Kimball, Inmon, dimensional modelling)
- Semantic Layer Architecture and centralised metrics layers
- Domain mapping from ontologies to dimensional schemas
- Advanced SQL for profiling, transformation, and performance tuning
- Data Transformation & CI/CD with tools like Dataform and Git/Jenkins
- Data Quality & Testing with automated validation scripts
- Metadata & Documentation management (data dictionaries, cataloging)
- Stakeholder translation of complex data structures
- Experience with Agile/Waterfall delivery frameworks
- Stakeholder communication
- Mentoring and coaching
- Collaborative mindset
- Data Modelling
- Semantic Layer Architecture
- Domain Mapping