76 to 94 of 94 Semantic Layer Jobs in London

evolving fast. With new tooling changing how we work, we are shifting from building data pipelines to building AI first data products, developing rich semantic and data context layers and empowering self-serve data users. This role leads that work across a defined business domain, directly managing a team … product level, with experience aligning analytics investment to domain prioritiesNice to HaveExperience in a marketplace, e-commerce, grocery or retail analytics contextFamiliarity with semantic and metrics layer tooling, or Python for data automation and pipeline workExposure to AI for Data, including agentic AI methods, semantic layers ...

role will play a critical part in moving Cluttons towards a fully integrated data environment, combining multiple data sources into a consistent reporting layer and delivering actionable insight to leadership. This is a hands-on leadership role, combining strategic design, technical delivery and stakeholder engagement. You will be positioned … lifecycle management. Lead the implementation and use of Microsoft Fabric as the core data platform. Define and manage data ingestion, transformation, modelling and semantic layers. Ensure compliance with information security and regulatory requirements. Reporting & Insight Deliver a standardised reporting suite covering pipeline, timesheets and financial performance across all business ...

hands-on senior role for a strong Python engineer with solid data engineering experience and practical exposure to AI agents. You will build catalogue, semantic, entitlement, and analytical layers that enable agents to work with enterprise data safely and effectively. Requirements: 6+ years building production software in Python, with … time-series stores). Experience in on-premise/regulated environments and their constraints (data residency, auditability, “golden copy never moves”). Familiarity with semantic layers/knowledge graphs and entity resolution. Exposure to policy-as-code (e.g. OPA) or data-access platforms. Awareness of how AI agents ...

intelligence platforms.Develop trusted, fact-based insight experiences that minimise AI hallucinations and provide clear, explainable outputs.Partner with data, BI, and technology teams to optimise semantic models for AI readiness.Apply data science, machine learning, NLP, and advanced analytics techniques to enhance insight generation and anomaly detection.Contribute to the organisation … Applied Analytics, or a related field.Proven ability to develop strategy and influence senior stakeholders, including executive-level audiences.Hands-on experience with enterprise data modelling, semantic layers, and business intelligence platforms (Power BI experience highly desirable).Experience working with LLMs, conversational AI, natural language analytics, or generative AI solutions.Deep understanding ...

Develop trusted, fact-based insight experiences that minimise AI hallucinations and provide clear, explainable outputs. Partner with data, BI, and technology teams to optimise semantic models for AI readiness. Apply data science, machine learning, NLP, and advanced analytics techniques to enhance insight generation and anomaly detection. Contribute … related field. Proven ability to develop strategy and influence senior stakeholders, including executive-level audiences. Hands-on experience with enterprise data modelling, semantic layers, and business intelligence platforms (Power BI experience highly desirable).Experience working with LLMs, conversational AI, natural language analytics, or generative AI solutions. Deep understanding ...

both current reporting and future analytical use cases. Key responsibilities include: Designing and governing enterprise-wide data models across Medallion Architecture layers. Creating curated semantic and analytical data models using dimensional modelling and Kimball methodologies. Defining data modelling standards, governance frameworks, and data feed specifications. Collaborating with Data Engineers … Modelling within large-scale enterprise environments. Dimensional modelling, star schemas, facts and dimensions, and Kimball methodologies. Designing and implementing Medallion Architecture frameworks. Building curated semantic layers within modern cloud data platforms. End-to-end data modelling from source systems through to analytics and reporting layers. Defining and implementing data ...

ROLES AND RESPONSIBILITIES: The Senior Analytics Manager will: Drive analytics innovation through AI, modern tooling, and evolving data platforms Define and implement strategies for semantic layers, metric governance, and self-service analytics Improve technical standards, scalability, and consistency across analytics practices Partner with senior leaders to shape the future … analytics, business intelligence, analytics engineering, or data strategy Advanced technical capabilities across analytics tools, coding, data modelling, and analytics architecture Experience working with semantic layers, metric governance, and self-service analytics environments Knowledge of modern data stack technologies, with exposure to dbt considered highly advantageous Demonstrated ability to influence ...

modeling, analytics engineering, dashboards, pipelines, governance, and performance optimization.Create documentation, samples, walkthroughs, and adoption assets for workflows across SQL, ELT/ETL, dbt, orchestration, semantic modeling, BI, and analytics application development.Partner closely with product, engineering, and field teams to identify high-value use cases, develop technical assets, and continuously … engineering, data transformation, orchestration, APIs, SDKs, notebooks, BI tools, and cloud data platforms.Strong understanding of SQL-centric developer workflows, including data modeling, query optimization, semantic layers, transformation frameworks, governance, observability, and dashboard or analytics consumption patterns.Experience building demos, prototypes, reference apps, or production-oriented examples for analytics, BI, data ...

analytics engineering, dashboards, pipelines, governance, and performance optimization. Create documentation, samples, walkthroughs, and adoption assets for workflows across SQL, ELT/ETL, dbt, orchestration, semantic modeling, BI, and analytics application development. Partner closely with product, engineering, and field teams to identify high-value use cases, develop technical assets … data transformation, orchestration, APIs, SDKs, notebooks, BI tools, and cloud data platforms. Strong understanding of SQL-centric developer workflows, including data modeling, query optimization, semantic layers, transformation frameworks, governance, observability, and dashboard or analytics consumption patterns. Experience building demos, prototypes, reference apps, or production-oriented examples for analytics ...

lifeDrive analytics-wide innovation, identifying practical opportunities created by AI, modern analytics tooling and evolving platform capabilities. Help define and execute our strategy for semantic layers, self-service analytics and metric governance. Raise technical standards across the analytics community, improving quality, consistency and scalability. Partner with Product, Data, Engineering … data modelling and analytics architecture. You're able to move comfortably between strategic discussions and technical implementation details. You have a strong understanding of semantic layers (e.g DBT metricflow or LookML), metric governance, self-service analytics and modern BI concepts, with a clear point of view on what good ...

empower self-service data literacy for PMs, design researchers and engineers.Design LLM-ready data foundations for onboarding analytics, including clean business entities, event schemas, semantic definitions, metadata, retrieval-friendly documentation, and evaluation datasets that make data usable by AI systems without losing governance or context.Partnering with your product manager … decomposition, experimentation, causal inference, or product opportunity sizing.Data product ownership: Experience building and owning production-grade analytical assets, such as dbt models, Airflow jobs, semantic layers, metric stores, governed dashboards, or trusted self-serve datasets.LLM-ready data thinking: You understand what makes data usable by AI systems: clean entities ...

payroll systems, and getting accurate data back out to them, reliably and securely, even at the volumes large customers bring. Build the data layer our agents act on: help connect our data to the AI agents and services we're building, including through MCP connectors, so they … format Experience with MCP server development , or different ways of preparing data for AI agents such as ontology modelling, context, knowledge graphs, and semantic layers. We don't expect all of it. We do expect curiosity about it. Why join us? Ben's customers are large enterprise employers ...

client-facing insight depends on engineers being able to find, understand, trust, and use financial datasets at scale. DataHub is Bloomberg’s experience layer for data. What GitHub is for code, DataHub is for data: the place where engineers discover datasets, understand schemas, request access, publish streams, subscribe … only a frontend role, and it is not only a backend infrastructure role. The experience is the full journey: the UI, the APIs, the semantic model, the provisioning flow, the feedback loops, the error states, the defaults, the automation, and the reliability of the systems underneath. The next frontier ...

while integrating people, AI agents and business processes across customer, employee and operational experiences.Shape enterprise AI and data foundations, including AI-ready data platforms, semantic layers, data products and trusted knowledge assets, alongside architectures that enable AI agents to securely interact with enterprise systems and processes.Establish responsible … major enterprise AI ecosystems such as OpenAI, Google, Anthropic and Microsoft.Experience shaping enterprise AI architectures and AI-ready data foundations, including cloud data platforms, semantic layers, data products, AI evaluation, security, governance and responsible AI.Significant consulting or senior client-facing experience, with the credibility to advise executive ...

integrating people, AI agents and business processes across customer, employee and operational experiences. Shape enterprise AI and data foundations, including AI-ready data platforms, semantic layers, data products and trusted knowledge assets, alongside architectures that enable AI agents to securely interact with enterprise systems and processes. Establish responsible … enterprise AI ecosystems such as OpenAI, Google, Anthropic and Microsoft. Experience shaping enterprise AI architectures and AI-ready data foundations, including cloud data platforms, semantic layers, data products, AI evaluation, security, governance and responsible AI. Significant consulting or senior client-facing experience, with the credibility to advise executive ...

will lead the development of trusted data foundations that make AI delivery faster, safer and easier to scale.Designing 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 analyticsShaping agentic architectures and engineering capabilities … processes and human decision-makers, while guiding 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:Significant experience in consulting or senior client-facing roles, acting ...

chemical markets get intelligence, and this role owns the platform foundations that make it possible. You will define the AI-ready data strategy, semantics layer, and MCP-first distribution approach that powers internal product teams, partner integrations, and AI-native customer experiences. This is a highly influential individual contributor … stakeholders to accelerate adoption, integrations, and commercial opportunities Requirements A track record shipping platform capabilities that others build upon, such as APIs, data products, semantic layers, evaluation frameworks, or comparable platform primitives Experience owning platform strategy, roadmap, and delivery outcomes in data, AI, developer platform, or similarly technical product ...

understand their business challenges, assess their knowledge landscape, and design production-ready solutions leveraging knowledge graphs, contextual data platforms, graph databases, GraphRAG, ontologies, and semantic technologies.Key ResponsibilitiesEngage with customers to identify business challenges that can be solved through contextual AI and knowledge-driven architectures.Assess enterprise knowledge assets, structured … transformation.Design, develop, and deploy production-ready enterprise knowledge systems and rapid Proofs of Value (PoVs).Architect enterprise knowledge solutions using knowledge graphs, graph databases, semantic layers, ontologies, metadata management, and contextual data platforms.Design GraphRAG, Hybrid RAG, Agentic RAG, and contextual retrieval architectures to improve AI reasoning, explainability, and accuracy.Develop ...

workflows. AI-readiness partnership. Work with Enablement so priority data is usable, discoverable and fit for AI; consume certified data products and the semantic layer. Execution as the function matures. Take direct ownership of AI delivery - prioritised use case to deployed, monitored, supported service - building the team and patterns … monitoring, and responsible-AI practice. AI governance experience and the ability to make data fit for AI, working through certified data products and a semantic layer. Specialty/London Market fluency (delegated authority, ceded reinsurance, entity resolution, Lloyd's data standards), or the ability to acquire it quickly. Ability ...