8 of 8 Semantic Layer Jobs in the UK

scalable data pipelines using Azure Data Factory and Databricks to automate data ingestion, transformation, and processing workflows. DCreate and maintain dimensional data models and semantic layers that support business intelligence and analytics use cases. Build and optimise data transformation workflows using dbt, SQL, and Python to create clean, well

Senior Analytics Engineer/Data Modeller Responsibilities Design, build, and maintain analytical and AI-ready data marts sourced from the enterprise data lakehouse. Develop semantic models and dimensional structures optimised for BI, dashboarding, and machine learning. Ensure documentation, governance, and data consistency across domains. Collaborate with data engineers … platforms such as Snowflake, Azure Synapse, BigQuery, or Redshift. Deep understanding of Data Vault, Kimball, and dimensional modelling techniques. Experience designing data marts and semantic layers for BI tools (Power BI, Tableau, Looker). Familiarity with analytics engineering tools including dbt, Airflow, and Git for version control. Excellent collaboration

design and development of complex, interactive dashboards (UI-based & LookML). Apply advanced LookML concepts (extends, refinements, templated filters, parameters, includes). Optimise semantic models and LookML performance. Implement data restriction functionality (sql_where, access_filters, row-level security). Establish and enforce governance and permissions , including multi-environment … deliver BI transformation projects, collaborating with executive-level customers. Required Qualifications 5+ years working with Looker and BigQuery. Strong proficiency in advanced LookML and semantic modelling. Experience in Looker performance tuning and model optimisation. Proven ability to develop complex dashboards with cross-filters, custom filters, and custom visualisations. Experience

MQTT, REST. Design and manage data lakes, warehouses, and streaming platforms for predictive analytics, digital twins, and operational intelligence. Define and maintain asset hierarchies, semantic models, and metadata frameworks for contextualized industrial data. Implement CI/CD pipelines for data workflows and ensure lineage, observability, and compliance across environments. … EventBridge etc. Industrial Data Integration:Familiarity with OT data schema originating from OSIsoft PI, SCADA, MES, and Historian systems. Information Modeling:Experience in defining semantic layers, asset hierarchies, and contextual models. Data Governance:Hands-on experience Data Quality:Ability to implement profiling, cleansing, standardization, and anomaly detection frameworks. Security

Senior Data Engineer (OLAP/Semantic Modelling) | 6 Month Contract | £550-600 p/day | Outside IR35 We're seeking a highly specialised Data Engineer with deep expertise in OLAP, Tabular, and Multidimensional modelling to join a technology-driven organisation on a long-term contract. If you have strong … SSAS, semantic modelling, and Excel cube integration experience - this role is for you . This is a hands-on, onsite position where you'll architect and optimise analytical models, build high-performance data pipelines, and design robust semantic layers that directly power business-critical reporting and Excel-based

this, they're building a central database spanning multiple datacubes across commercial, advertising, and revenue functions. They previously worked with advisory specialists to create semantic layers, but there are gaps in how this translates into clear, usable insight - which is why they need a strong analyst to take ownership

quality, transformation, security, batch management, monitoring, alerting, and cost control are efficient. Design and help build the Data Mesh – ensuring data is processed through semantic layers and can be modelled effectively. Suggest improvements for automation and cost savings. Mentor and lead teams of engineers, checking and reviewing code

quality, transformation, security, batch management, monitoring, alerting, and cost control are efficient. Design and help build the Data Mesh – ensuring data is processed through semantic layers and can be modelled effectively. Suggest improvements for automation and cost savings. Mentor and lead teams of engineers, checking and reviewing code