4 of 4 Remote/Hybrid Semantic Layer Jobs in the North of England

growing brand runs on. You will own what happens to data once it lands in our data warehouse, including transformation in Dataform, modelling, the semantic layer, and the reporting the business runs on. This is one of two engineering roles we are hiring into the Business Intelligence team. … behind it reflect how the business wants to measure itself, and every metric means one thing. What you will be doing Own the transformation layer in Dataform end to end: the models, dependency graph, tests and documentation between raw BigQuery data and everything the business sees. Build the semantic ...

ownership of our internal reporting tools and partner-facing data products, including OSP Insights. By establishing robust evaluation frameworks and aligning semantic architecture with business outcomes, you will ensure our global partners and internal stakeholders derive maximum value from certified operational data. Key Responsibilities: Define and execute the group … analytics, using Claude and Model Context Protocol (MCP) to enable self-serve access to certified data. Collaborate with Analytics Engineering to shape the unified semantic layer migration onto dbt MetricFlow. Establish evaluation frameworks, telemetry, and logging to rigorously assess AI accuracy, system performance, and metric trust. ...

real-time data-processing solutions. Lead the design of a Customer Data Platform. Build and evolve a lakehouse architecture using AWS technologies. Create governed semantic models, consistent metrics and clear data definitions. Enable secure, self-service data analysis using modern AI tooling. Automate manual, repetitive or fragile data processes. … open table formats such as Iceberg. Medallion architecture and dimensional data modelling. Strong SQL and Python. SQL Server, SSIS/SSDT and Power BI. Semantic layers, common metrics and data glossaries. Data governance, lineage, retention, classification and auditability. Managing and developing a small technical team. You should also ...

foundations that make AI delivery faster, safer and easier to scale. Leading design and delivery teams to build AI-ready data foundations, platforms and semantic layers that provide trusted business context, reusable knowledge assets and reliable access to enterprise information for AI and analytics Designing and embedding engineering capabilities … human decision-makers, while supporting technology and platform decisions. Understanding of the foundations required to build trusted enterprise AI, including cloud data platforms, semantic layers, data products, AI evaluation, security, governance and responsible AI practices. Consulting & Client Leadership: Experience in consulting or client-facing roles, building trusted senior client ...