Design, build, and govern our enterprise gold data and semantic layer that serves as the single, validated, and trusted source of truth for analytics across the AME region.
The role bridges business and data engineering by translating business needs into reusable, standardised data models that enable self-service analytics and consistent KPI usage. This layer supports analytics teams, business users, and future GenAI-driven analytics solutions.
Key Requirements
Design, build, and maintain our analytics gold data layer in Databricks, ensuring business-aligned, scalable, and reusable datasets
Develop and govern the semantic layer (e.g. Databricks Unity Catalog Metric Views, Power BI semantic models) as the single source of truth for analytics consumption
Define and standardise business KPIs and metric definitions, facilitating alignment across stakeholders and OpCos to eliminate inconsistencies
Enforce strong data governance, modelling standards, naming conventions, and data quality controls, including testing, validation, and documentation of datasets
Partner with data engineering to ensure upstream data is structured, reliable, and fit-for-purpose for downstream analytics consumption
Design and govern interfaces for downstream consumption, ensuring analytics, BI, and data science outputs are consistently structured and business-readable
Enable scalable self-service analytics, providing datasets that are intuitive, well-structured, and optimised for business users and analytics teams
Prevent fragmentation by driving adoption of a single, governed gold layer, reducing duplication and shadow datasets across teams
Collaborate closely with business stakeholders to understand data needs, KPIs, and analytical use cases, translating these into reusable data models
Continuously improve the semantic layer to support advanced analytics and conversational interfaces, including preparing models that are machine-consumable for GenAI and natural language usage
Monitor, troubleshoot, and optimise performance, data quality, and reliability across the gold and semantic layers
Contribute to defining and evolving analytics engineering standards, best practices, and ways of working within the BI chapter
Education and Experience
Qualifications
Graduate degree or formal qualification in a relevant field (Equivalent certifications or relevant professional experience considered)
Must have:
5+ years of experience in analytics engineering, BI engineering, or data modelling roles
Strong expertise in data modelling, including dimensional modelling (star/snowflake schemas) and semantic layer design
Advanced SQL skills and experience working with cloud data platforms (e.g. Databricks, lakehouse architectures, Power BI/Fabric)
Proven experience building and governing semantic models and reusable datasets for analytics and BI
Strong experience defining and implementing business KPIs and metric standardisation
Experience enforcing data governance, quality frameworks, testing, validation and documentation practices
A quality-first mindset - testing, documentation, and lineage as integral to the work, not overhead added at the end
Ability to translate business requirements into scalable, reusable data products
Nice to have:
Experience working with FMCG data domains
Experience using Databricks Metric Views or similar semantic layer technologies
Experience building enterprise self-service analytics environments
Experience designing or supporting GenAI, agentic or conversational interfaces leveraging business data and MCP (Model Context Protocol)
Knowledge of Power BI advanced semantic modelling (DAX, RLS)
Familiarity with evolving semantic layer and data product patterns in modern data stacks
Closing Date: 03/07/2026
