51 to 75 of 94 Semantic Layer Jobs in London

platform maturity. Responsibilities include: Leading end-to-end Tableau development across Tableau Desktop, Tableau Prep, and Tableau Cloud Designing and maintaining scalable data sources, semantic layers, and reporting solutions Defining and embedding Tableau development best practices across the business Building high-quality dashboards that deliver actionable business insights Writing ...

performance, media ROI, trade effectiveness, sales analytics and category growth.* Advise on modern data foundation principles, including data governance, standardisation, metadata, lineage, quality, interoperability, semantic layers, data products and reuse.* Support the design of scalable data operating models, including ownership, stewardship, governance forums, decision rights and adoption mechanisms.* Bring ...

would struggle to find anywhere else in this industry. You will sit between data engineering and the business. That means turning commercial questions into semantic models, curated datasets and reusable data assets that leaders across EMEA and APAC rely on, working with our engineers on the upstream design that … would struggle to find anywhere else in this industry. You will sit between data engineering and the business. That means turning commercial questions into semantic models, curated datasets and reusable data assets that leaders across EMEA and APAC rely on, working with our engineers on the upstream design that ...

more important than ever!As Director of Data Products, you will own the platform, the products, the tools, the processes and the self-serve layer, so that the answer arrives before the request does.You will be a force-multiplier for the quality, velocity and impact of Multiverse's decision … senior stakeholders across the org to coordinate a complex set of competing needs.What you'll ownData leverage across MultiverseThe platform, products and self-serve layer that ~800 people use to find, understand and act on our data — reducing the dependency on Data & Insight to answer questions others could answer ...

structure, and that's why most enterprise AI is failing. The shift from copilots to autonomous agents is creating an entirely new infrastructure layer, and we're building it. zaimler is the context infrastructure for the agentic era: a platform that automatically discovers domain knowledge, maps relationships, and gives … agents the semantic understanding to operate with precision at scale. Imagine knowledge graphs that support real-time inference, built for systems that need to reason, not just retrieve. zaimler was founded by Biswajit Das (ex-VP Engineering, Truera), a Data Infra veteran and former Chief Architect at Visa ...

data sources. You'll have the opportunity to: Design scalable data architecture to support future growth and new data sources. Build intuitive data models, semantic layers, and reporting structures. Improve data quality, accessibility, and governance. Simplify complex data environments through clear documentation and data mapping. Troubleshoot and enhance data ...

hold a team accountable to them.Strong stakeholder management and translation skills, converting business and analytics demand into well-formed requirements.Understanding of data-product thinking, semantic layers and self-service, and how certified products displace tactical reporting.Specialty/London Market business fluency (underwriting, delegated authority, finance, exposure), or the ability ...

configure AI-powered data management capabilities and agentic solutions.Enable AI-ready data by establishing standards for metadata, business glossaries, taxonomies, data products and semantic layers that support both trusted business consumption and AI model interoperability.Collaborate with architecture, engineering and platform teams to design target-state data architectures that enable ...

configure AI-powered data management capabilities and agentic solutions. Enable AI-ready data by establishing standards for metadata, business glossaries, taxonomies, data products and semantic layers that support both trusted business consumption and AI model interoperability. Collaborate with architecture, engineering and platform teams to design target-state data architectures ...

DoingAnalytics Engineering and Data ModellingDesign, build, and maintain analytics‐ready data models using dbt, following best practices such as modularity, reusability, and clear semantic layering.Lead the development of greenfield analytics assets, from raw source ingestion through to curated, business‐facing models.Apply strong data modelling techniques, including dimensional modelling, facts … dimensions, and other appropriate design patterns.Own and evolve the analytics layer architecture, ensuring it scales with data volume and business complexity.Ensure data quality and trust through testing, documentation, and observability within dbt.Business PartnershipPartner closely with business stakeholders, analysts, and product teams to understand requirements and translate them into well ...

DoingAnalytics Engineering and Data ModellingDesign, build, and maintain analytics‐ready data models using dbt, following best practices such as modularity, reusability, and clear semantic layering. Lead the development of greenfield analytics assets, from raw source ingestion through to curated, business‐facing models. Apply strong data modelling techniques, including dimensional … modelling, facts and dimensions, and other appropriate design patterns. Own and evolve the analytics layer architecture, ensuring it scales with data volume and business complexity. Ensure data quality and trust through testing, documentation, and observability within dbt. Business PartnershipPartner closely with business stakeholders, analysts, and product teams to understand ...

solutions that drive business outcomes. Responsibilities Build and maintain scalable data pipelines integrating customer, campaign, and operational data into the CDP and marketing data layer Design and manage data models to support segmentation, lifecycle analytics, attribution, and KPI measurement Ensure data quality, consistency, deduplication, and optimisation for analytics … resolution, consent management, and audience readiness Develop event streams and triggers to support customer journey orchestration and real-time decisioning Provide structured datasets and semantic layers for dashboards and advanced analytics Implement data governance practices, including lineage, cataloguing, validation, and GDPR/PII compliance Partner with analytics, MarTech ...

team accountable to them. Strong stakeholder management and translation skills, converting business and analytics demand into well-formed requirements. Understanding of data-product thinking, semantic layers and self-service, and how certified products displace tactical reporting. Specialty/London Market business fluency (underwriting, delegated authority, finance, exposure ...

Define and maintain current and target‐state enterprise data architectures. Develop conceptual, logical, and physical data models. Establish enterprise data domains, canonical models, ontologies, semantic layers, and metadata frameworks. Create data architecture roadmaps aligned to business and digital strategy. Drive data governance, lineage, quality, observability, and master data management … data teams. Essential Skills & Experience Strong enterprise‐level Information or Data Architecture experience. Expertise in data strategy, governance, metadata management, ontologies, knowledge graphs, and semantic modelling. Experience designing end‐to‐end data architectures across complex organisations. Strong Azure data platform experience, including Databricks. Experience delivering large‐scale transformation programmes. ...

Work with stakeholders to gather, analyse and document reporting requirements. Translate business needs into practical and user-centred reporting solutions. Develop and maintain scalable semantic models and datasets using Power BI best practice. Create and optimise DAX measures, calculations and business metrics. Use Power Query and data transformation techniques … BI. Experience working with Dataverse, Dynamics 365 and Power Platform data sources to develop reporting and analytical solutions. Strong experience creating data models and semantic layers to support reporting and analytics. Advanced knowledge of DAX and experience developing complex calculations and measures. Strong experience using Power Query and data ...

translation of business requirements into rigorous data structures in collaboration with engineering and domain teams Guide teams on the design and effective use of semantic data models, including their application to agentic AI solutions Semantic & Knowledge Modelling Integrate semantic layers with business glossaries, data catalogues, and lineage … tooling Design and maintain semantic models to support agentic-AI capabilities Help develop ontologies, taxonomies, and controlled vocabularies underpinning enterprise metadata and search Define semantic modelling standards and patterns to ensure consistent meaning, interoperability, and AI readiness across data products Guide and control the creation of data domains ...

Senior Software Engineer (Backend/Full-Stack, AI-Native)About Us We're rethinking enterprise integration for developers and AI agents with a semantic layer: describe the data you need, and the platform gets it - fast, deterministic, secure, self-repairing, auditable, scalable. Already live with banks and financial ...

gold layers within a Medallion Architecture. Create scalable curated data models using dimensional modelling, star schemas, facts and dimensions, and Kimball methodologies. Define semantic data layers, modelling standards, and governance frameworks to improve consistency, quality, and reusability. Manage logical and physical data models, including metadata, business glossaries, data flows … SKILLS AND EXPERIENCE: Strong hands-on experience in Data Architecture and Data Modelling within enterprise-scale environments. Excellent knowledge of dimensional modelling, star schemas, semantic layers, and Kimball methodologies. Experience designing and implementing curated data models within cloud-based data platforms. Strong understanding of logical, physical, transactional, analytical ...

enterprise consistency and engineering excellence.Key Responsibilities:1. Enterprise Data Modelling• Lead the enterprise approach to analytical data modelling, including domain models, dimensional models and semantic layers.• Establish reusable modelling patterns and common business entities that create consistency across Data Products.• Reduce duplication and improve the performance, scalability and maintainability … Head of Analytics Engineering to shape the technical roadmap and future engineering capability.Key Technology Areas:Snowflake • DBT • SQL • Python • Git/CI/CD • Semantic Layers • Data Contracts • Data Quality & Observability • Snowflake Cortex • LLMs & AI Agents • AWSSkills & Experience:Must Have• Strong hands-on Analytics Engineering/Data Engineering experience ...

Codify the firm's unwritten methods and judgements into the coworkers and playbooks that run at machine speed. Anchor them through the ontology and semantic layers in Celeste so the agents reason the way its own people do. Earn adoption – Lead the actions that earns Celeste's adoption, from ...

Codify the firm’s unwritten methods and judgements into the coworkers and playbooks that run at machine speed. Anchor them through the ontology and semantic layers in Celeste so the agents reason the way its own people do. Earn adoption – Lead the actions that earns Celeste’s adoption, from ...

service model that serves Risk and Compliance.• Delivery. Deliver priority data products and automation. • Operating disciplines. Own the data-product operating disciplines, product management, semantic and taxonomy ownership, and AI access and insight assurance.• Outcomes. Measure and report success by decision-influence and accountability outcomes, not build volume.Key Relationships … Work With• LLMs and agentic frameworks• RAG architectures• AI agents and orchestration (e.g. LangChain/LangGraph)• Python and modern data engineering• Snowflake and governed semantic layers• AWS/GCP cloud services• Vector databases• Data pipelines and MLOps tooling• Business intelligence and visualisation tools#LI-AB3What ...

translation of business requirements into rigorous data structures in collaboration with engineering and domain teamsGuide teams on the design and effective use of semantic data models, including their application to agentic AI solutionsSemantic & Knowledge ModellingIntegrate semantic layers with business glossaries, data catalogues, and lineage toolingDesign and maintain semantic models to support agentic-AI capabilitiesHelp develop ontologies, taxonomies, and controlled vocabularies underpinning enterprise metadata and searchDefine semantic modelling standards and patterns to ensure consistent meaning, interoperability, and AI readiness across data productsGuide and control the creation of data domains that align with the banks underlying capabilitiesData Engineering ...

Staff Analytics Platform Engineer, you’ll provide technical leadership across the entire analytics platform, from ingestion and transformation tooling to Snowflake architecture, semantic modelling, and secure analytics environments. You’ll define the patterns, standards, and infrastructure that enable analysts, data scientists, and product teams to build reliable, scalable analytics … with you. What you’ll be doing Co‐owning the analytics platform architecture, designing end‐to‐end solutions across Snowflake, ingestion pipelines, dbt infrastructure, semantic layers, and secure analytics environments. Defining and driving the long‐term technical vision for the analytics platform, ensuring it is scalable, cost‐efficient, secure ...

within the CDPJourney Orchestration Data Readiness: Enable event streams, triggers, and monitoring to support accurate, timely customer journeysAnalytics & Dashboarding Enablement: Provide structured datasets and semantic layers for reporting and dashboards, aligned with KPI frameworksData Governance & Quality Assurance: Maintain data quality, lineage, and compliance with GDPR and PII standardsCollaboration & Strategic … across digital, CRM, transactional, and behavioural data; support identity resolution, consent handling, audience activation, journey events, trigger logic, and behavioural signalsBuild data marts and semantic layers for dashboarding and BI tools such as Power BI or Tableau; structure datasets optimised for KPI frameworks, attribution, funnel, and lifecycle analyticsApply data ...