13 of 13 Remote/Hybrid Semantic Layer Jobs

/star schema) for analytics and reporting Provide technical guidance and support to BI Developers during delivery (non-managerial) Contribute to Power BI/semantic layer design to support enterprise reporting Support integration of data from multiple SaaS and third-party systems Ensure best practices across data quality … desirable) Strong background in data warehousing and BI environments Excellent dimensional modelling (Kimball) experience Experience working closely with BI/reporting teams (Power BI, semantic models, DAX) Confident working across end-to-end data pipelines and integration patterns Strong SQL skills with experience optimising queries and datasets Comfortable guiding ...

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 ...

deliver a step-change in how insight is generated, distributed and consumed across the Group, moving to a unified, conversational, proactive AI insight layer built on the Central Data Layer (CDL). This role goes beyond traditional BI or analytics leadership. It requires redefining the paradigm of insight … consumption from pull-based reporting to AI-driven push, nudging and decision support, enabled through semantic abstraction, conversational access, and multi-channel delivery. You will lead the development of the vision and associated architectures (including Claude-enabled interaction models), ensuring these are translated into production-grade, scalable capabilities adopted ...

Translate roadmap into phases of discovery, design, implementation, and validation, ensuring each phase has defined deliverables, owners, and timelines Define business logic and semantic layers for Awin's financial and business analytics Collaborate with and serve as the first point of contact for business stakeholders to understand and solve ...

strategy and influence data governance at scale Involvement in a major cloud migration programme Exposure to emerging areas such as AI-enabled analytics and semantic layer development Clear progression within a structured framework How to Apply If you are an expert in Tableau, apply below ...

delivery focused Data Modeller who combines strong architectural thinking with genuine hands-on capability. You'll be comfortable moving between business requirements, data modelling, semantic layer design and technical implementation, helping to drive a best in class data platform. Key Knowledge Designing and delivering enterprise scale data models … Snowflake Working hands-on across data warehousing, data integration and data exchange solutions Defining and implementing logical and physical data models Developing and optimising semantic layers to support reporting, analytics and self-service consumption Collaborating with Data Architects, Engineers and Business Stakeholders to translate requirements into scalable solutions Supporting ...

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 ...

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 ...

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 ...

only for measurement strategy but also for helping shape the future customer and marketing technology ecosystem. Major investment is underway across customer platforms, semantic layers, data infrastructure and marketing technology, requiring an individual who is equally comfortable discussing econometric methodologies, technology architecture and commercial outcomes. Perhaps most interestingly ...

. Set up tracking and monitoring for new launches and changes, and quantify and measure their impact on KPIs. Build and maintain a reporting layer, with automated alerting and root cause analysis that flags when KPIs move unexpectedly and why. Design, run, and interpret experiments, from A/… tests to quasi-experimental methods like difference-in-differences when randomisation not possible. Model robust, trusted data in our dbt and BigQuery layer, safeguarding its quality through testing, documentation, and certification in collaboration with engineering. Create reusable, AI-assisted analytics skills that scale the team's impact and help ...

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 ...

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 ...