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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Specialist - Data Analytics Engineer - **Company:** Morrisons - **Location:** Bradford, UK - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Test Suite, Abstraction Layers, Agile Methodology, Amazon Web Services, Data Analysis, Architectural Patterns, Microsoft Azure, BigQuery, Cloud Computing, Continuous Integration, Data Architecture, Data Validation, Data Dictionary, Data Infrastructure, Data Transformation, Data Profiling, Data Structures, Relational Databases, Database Testing, Python (Programming Language), Metadata, Metadata Repositories, Performance Tuning, Raw Data, Release Management, SQL Databases, Technical Data Management Systems, Scripting, Google Cloud, Sql Optimization, Google Data Studio, Git, Data Layers, Data Analytics, Tools for Reporting, Domain Driven Design, Looker Analytics, Software Version Control, Jenkins, Programming Languages - **Published:** September 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e2b3f98d6ca85c67 ## About the Role 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 assets. * Stakeholder Translation: Excellent ability to communicate complex data structures to non-technical business users. * Delivery Frameworks: Comfortable working within Agile and Waterfall project delivery methodologies. Desirable: * Cloud Infrastructure: Hands-on experience with Google Cloud Platform (BigQuery, GCP storage) or equivalent cloud providers (AWS/Azure). * Programming Languages: Proficiency in Python, R, or general-purpose scripting languages. * Analytics Tools: Exposure to visualisation software (e.g., Looker, Looker Studio) and proficiency in Look ML. * Domain Experience: Experience in FMCG, retail, manufacturing, supply chain, or logistics environments. * Modern Architectures: Exposure to Data Mesh concepts, domain-driven data ownership, or data catalog tools. ## Description * 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. 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