Data Specialist - Data Analytics Engineer

More about the role

Our Data & Analytics team creates value for Morrisons by making sure we have the right data in the right place, transformed into clean, accessible structures ready for reporting, data product creation and enabling advanced decision-making. Utilising our Google Cloud data platform, the Analytics Engineer transforms raw data from across our operations from field to fork into optimised, reliable, and well-documented data models and data products.

You will bridge the gap between technical data pipelines and business insight, enabling analysts and leaders to make faster, better-informed decisions. Accountabilities The role will have the following accountabilities:

Customer Build trusted relationships with data consumers and business analysts across internal functions.

Translate business processes, operational requirements, and user feedback into robust data architecture and optimised analytical datasets.

Own data model delivery for the domain area, ensuring documentation and data definitions are accurate and accessible.

Colleague Champion data literacy, modelling best practices, and standard transformation guidelines across the organisation.

Mentor trainee data practitioners on SQL standards, data testing, version control, and business domain context.

Facilitate clear communication between technical engineering teams and non-technical business stakeholders.

Cash & Profit Support a lean working culture by building optimised, reusable conformed data layers that minimise processing waste and lower GCP cloud compute costs.

Reduce duplicate engineering efforts by consolidating redundant metrics into centralised analytical data models.

Operational Design, code, test, and deploy secure, maintainable data models and transformations using modern data tools (e.g., SQL, Dataform) in Google Cloud Platform.

Build, maintain, and govern the centralised semantic layer (e.g. LookML, or cloud data catalog metrics) to deliver consistent, trusted business definitions across all reporting platforms.

Map enterprise domain ontologies into relational data models, ensuring business concepts and their relationships accurately mirror real-world retail and supply chain processes.

Implement and maintain metadata repositories, data dictionaries, and lineage tools within the enterprise data catalog.

Define test conditions and execute automated data quality scripts to validate datasets before production release.

Continually refine existing models to adapt to operational changes, platform upgrades, and new business priorities.

More about you

What we’re looking for:

Skills, Qualifications and Experience As a Data & Analytics Specialist - Analytics Engineer  you will have experience of:
Data Modelling:
Proven experience building and maintaining data models using recognised patterns (e.g., Kimball, Inmon, dimensional modelling).Semantic Layer Architecture:
Hands-on experience building and managing centralised semantic layers, metrics tiers, or business abstraction layers in modern data tools.

Business Ontology & Domain Mapping:
Proven ability to analyse complex enterprise domain ontologies and translate entity relationships (e.g., supplier-to-product-to-store hierarchies) into structured dimensional schemas.

Advanced SQL:
Exceptional SQL capabilities for data profiling, transformation, aggregation, and performance tuning.

Data Transformation & CI/CD:
Practical experience using transformation tools (e.g. Dataform) and version control workflows (Git, Jenkins) to deploy maintainable code.

Data Quality & Testing:
Experience writing automated data validation scripts and test suites to verify accuracy and structural integrity.

Metadata & Documentation:
Ability to design and manage metadata, data dictionaries, and data cataloging…