10 of 10 Semantic Layer Jobs in the UK excluding London

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. ...

Tableau access management and application management Experience working with enterprise data discovery, analytics, and BI software tools such as Tableau, PowerBI and others for semantic-layer-based data discovery. Experience in enabling the BI self service capability for the organisation Experience and knowledge of big data platforms ...

following technologies: Data Processing: Apache Spark/PySpark Workflow Orchestration: Apache Airflow Containerisation: Kubernetes Cloud Platform: Microsoft Azure Data APIs & Semantic Layer: CubeJS Programming Languages: Python, SQL Essential Skills & Experience Degree in a STEM discipline, ideally Computer Science, Software Engineering, or a related field. Minimum of 5 years … Experience designing and building scalable, production-grade data pipelines. Desirable Experience Experience working within cloud-native Azure environments. Knowledge of modern analytics platforms and semantic data layers. Previous mentoring or technical leadership experience. Apply If you're a passionate Senior Data Engineer looking to work on complex data challenges ...

transformation programmes. What you'll be doing Supporting solution architecture and technical design Building scalable, production-grade data platform Developing data models, pipelines and semantic layers Working directly with clients to understand requirements and shape delivery roadmaps What we're looking for 24 years' experience in data engineering ...

architecture and roadmap. Design, develop and optimise data pipelines, lakehouse and warehouse solutions to support reliable data delivery. Build and maintain scalable data models, semantic layers and reporting solutions. Work extensively with SQL, Python and the wider Microsoft data ecosystem. Develop and enhance Power BI reporting to provide meaningful … automation. Good understanding of lakehouse and data warehouse architectures. Experience developing and maintaining data pipelines and orchestration processes. Knowledge of Power BI, including semantic models, DAX and reporting. Experience with Git, CI/CD and modern software engineering practices. An understanding of data governance, security, quality and platform performance. ...

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 ...

role in developing high-quality, analytics-ready data across its modern data platform. This is an opportunity to take ownership of the curated data layer within a Databricks Lakehouse environment, translating complex business requirements into robust data models and transformation pipelines that support reporting, analytics, and business decision-making. … Responsibilities: Lead the design and development of curated, analytics-ready dimensional and semantic data models. Build, maintain and optimise complex SQL and PySpark transformation pipelines within Databricks. Define and maintain KPI definitions, conformed dimensions and consistent business logic across data domains. Ensure data quality, integrity, testing, observability and performance ...

value, technical feasibility, data readiness and strategic alignment. Collaborate closely with data engineering, data product and technology teams to ensure data infrastructure, pipelines and semantic layers support advanced analytical needs. Translate complex data and modelling outputs into clear, compelling narratives tailored to executive, senior leadership and operational audiences. Define … processes, data quality, metric definition, analytical engineering principles and collaboration with data engineering teams. Experience designing, governing and scaling analytical products, insight frameworks, semantic layers, dashboards or measurement solutions used across multiple business areas. Advanced hands-on capability in SQL, Python or R for advanced analysis, automation, statistical modelling ...

Management (MDM), Data Quality & Observability, Metadata Management and Data Products as well as supporting organisations adoption of AI including Agentic Data Management, AI Observability, Semantic Layers and Ontologies. We are looking for candidates that have deep experience in one or more core capabilities below and wants … reinventing Data Management & Governance for the AI era. AI Data Management: awareness or experience of areas such as Agentic Data Management, AI Observability, semantic layers, knowledge graphs and ontologies. Data Governance & Strategy : experience supporting the design or implementation of governance frameworks, policies, standards, controls, ownership and stewardship models. Data ...

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 ...