20 of 20 Remote/Hybrid Permanent Semantic Layer Jobs

/ELT data pipelines. Good understanding of PySpark, Spark SQL, Python, and SQL. Strong knowledge of data warehouse, data lake, data mart, and semantic layer architecture. Experience in defining data quality, data validation, reconciliation, audit, and exception handling frameworks. Experience with data governance, metadata management, data lineage, data ...

team of data engineers (minimum 2) Work closely with stakeholders to translate business requirements into scalable data solutions Enable downstream analytics, including Power BI semantic models and dashboards Contribute to long-term AI and data innovation initiatives YOUR SKILLS AND EXPERIENCE: 6+ years' experience in data engineering Strong expertise … data modelling (dimensional modelling and semantic layer design) Hands-on experience with Microsoft Fabric and/or Azure data services Proven end-to-end delivery across the data lifecycle (through to visualisation) Experience managing or mentoring engineers Strong stakeholder engagement and communication skills Nice to have: Experience working ...

cloud data platforms, lakehouse architectures, and analytics ecosystems Lead architecture for data modelling, warehousing, and ETL/ELT pipelines Design and oversee BI and semantic layer architecture (Power BI and similar tools) Define standards for data governance, security, lineage, and data quality Engage senior stakeholders to shape data ...

Engineering to shape how data is structured, defined, and consumed across the business. You'll be responsible for designing and building the curated and semantic layers that sit between raw data ingestion and business consumption — the models that power self-serve reporting, BI tooling, and emerging AI and agentic … tools that rely on it. Responsibilities Design, build, and maintain curated data models in dbt across staging, intermediate, and mart layers Develop semantic layer definitions — metrics, dimensions, and business logic — that serve as a single source of truth for BI and analytics Translate business concepts and rules into ...

Snowflake Develop reusable data assets for downstream reporting and analytics Troubleshoot and resolve data quality or performance issues Improve the Reporting & Analytics Layer Design a clean, scalable semantic layer for reporting Standardise key business metrics and definitions Support transition away from legacy report-first design toward model ...

consistent understanding and reuse - Ensuring data models are optimised for Power BI consumption, including considerations for performance, aggregation, and usability - Supporting the design of semantic layers and ensure alignment between data models and reporting models - Working closely with Business Analysts and Power BI Developers to ensure data structures meet ...

modelling standards, including dimensional modelling approaches. Develop CI and CD pipelines to enable reliable deployment and testing of data products. Create and maintain a semantic layer for self-service analytics and reporting. Partner with stakeholders across multiple business areas to translate data needs into solutions. Deliver insights … data pipelines using Spark and Python or SQL. Strong understanding of data modelling, including star and snowflake schemas. Experience working with BI tools and semantic layers such as Looker or similar platforms. Familiarity with version control and CI or CD workflows. Ability to communicate complex technical concepts ...

modelling standards, including dimensional modelling approaches. Develop CI and CD pipelines to enable reliable deployment and testing of data products. Create and maintain a semantic layer for self-service analytics and reporting. Partner with stakeholders across multiple business areas to translate data needs into solutions. Deliver insights … data pipelines using Spark and Python or SQL. Strong understanding of data modelling, including star and snowflake schemas. Experience working with BI tools and semantic layers such as Looker or similar platforms. Familiarity with version control and CI or CD workflows. Ability to communicate complex technical concepts ...

modelling standards, including dimensional modelling approaches. Develop CI and CD pipelines to enable reliable deployment and testing of data products. Create and maintain a semantic layer for self-service analytics and reporting. Partner with stakeholders across multiple business areas to translate data needs into solutions. Deliver insights … data pipelines using Spark and Python or SQL. Strong understanding of data modelling, including star and snowflake schemas. Experience working with BI tools and semantic layers such as Looker or similar platforms. Familiarity with version control and CI or CD workflows. Ability to communicate complex technical concepts ...

Lead & Mentor Manage and develop a team of Analytics Engineers, helping establish best practices around modelling, testing, documentation, and delivery standards. Own the Semantic Layer Shape and maintain trusted business definitions and scalable dbt models powering reporting, experimentation, and operational analytics. Build Self-Serve Analytics Enable teams across ...

trusted partner to senior leaders, influencing at executive level. Experience defining and embedding end‐to‐end BI delivery methodologies. Strong understanding of data governance, semantic models and trusted data principles. Hands‐on experience delivering BI in an agile, iterative environment. Experience working within insurance or wider financial services data … reporting estates. Exposure to AI‐augmented analytics or automated insight generation. Experience leading BI teams through organisational change or consolidation. Familiarity with enterprise semantic layer technologies. Responsible Artificial Intelligence usage. What We Will Offer You Recognised and rewarded for a job well done, we have a range ...

working with technology to configure AI-driven Data Management agents Enable AI-ready data by defining standards for metadata, data taxonomies, data products and semantic layers that support both human understanding and model consumption. Collaborate with architecture and engineering teams to design target state architectures that enable AI data ...

Business to ensure analytics is embedded in every decision Drive "Data as a Product" , owning the evolution of our data models and semantic layer, ensuring teams can reliably self‐serve from high‐quality data products Champion governance and guardrails , establishing rigorous standards for modelling, semantics, testing, and auditability ...

scaling of CIFF’s operations and collective intelligence (through data visualisation and similar platforms. Support the Enterprise Architect in modernising the BI ecosystem (e.g., semantic layer and self‐service BI) and deliver real‐time impact dashboards, programme scorecards, and portfolio insights. Work with missions, programme teams to embed ...

Azure Environment. Key Criteria: Strong knowledge of: Cloud-native data platforms (e.g., lakehouse, warehouse, streaming systems) Modelling approaches (medallion, dimensional, event-driven, semantic layers) Data governance frameworks and metadata tooling Data lineage, cataloguing, discovery, and documentation Data quality and observability tooling Data access/security patterns Skilled in standardisationcreating ...

data pipelines within Microsoft Fabric, implementing and maintaining medallion architecture standards across ingestion, transformation, and presentation layers Develop and optimise trusted data models and semantic layers that enable high-quality reporting, self-service analytics, and advanced business insight Collaborate closely with stakeholders across the organisation to translate business requirements … skills, including developing, optimising, and troubleshooting queries to support data transformation and analytical models Confident in building and maintaining robust data models and semantic layers that support high-quality reporting, self-service analytics, and advanced insight using tools such as Power BI or similar analytics platforms Experience working with ...

execute a defined plan. You will be working across two fronts simultaneously: building the core platform capabilities — the virtualized data lake, Common Data Layer, and data contract framework — that give WTW a unified, AI-ready data foundation; and embedding directly within product domains to deliver the high-quality, reusable … Databricks data engineering and ML stack. Strong command of DBT for modular, testable, and well-documented data transformation; a clear point of view on semantic modeling and metric layer design. Fluency in Python and SQL; comfort with Spark for large-scale data processing and transformation. Experience with modern ...

operations and commercial tea ms. Roles & Responsibili tiesDefine the technology stack and architecture for the organisation's enterprise data wareh ouseEstablish data modelling standards, semantic layers and reusable data setsOversee ETL pipelines across ERP, CRM and operational sys temsImplement data quality, governance and DataOps best pract icesLead and direct … inesExperience working with ERP and financial data environm entsExperience leading distributed, offshore or third-party delivery t eamsExperience defining data architecture, modelling standards and semantic la yersKnowledge of data governance frameworks, data quality controls and data stewardship princi plesWhat You GetHybrid working (3 days per week ...

growing hub-and-spoke data model, working closely with embedded data roles in product/platform squads while owning the central foundations: certified metrics, semantic layers, trusted insight, governance, standards and decision-support. What you’ll be doing: Leading and developing a team of data specialists Partnering with senior … commercial stakeholders across customer, pricing, marketing and finance Turning ambiguous business questions into clear, actionable insight Owning trusted data foundations, including certified metrics, semantic layers and insight standards Supporting AI and LLM-enabled access to data, with the right guardrails in place Driving better data culture, stronger decision-making ...

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 … 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 ...