5 of 5 Remote/Hybrid Permanent Semantic Layer Jobs

machine learning use cases in a structured, governed, and scalable way. This includes end-to-end accountability for data ingestion, transformation, lakehouse design, semantic modelling, and the development of reliable data products. The role ensures business data is accessible, consistent, and trusted, with clearly defined metrics that enable high … Manage ingestion, transformation and storage of data across the platform. Ensure the platform supports both analytical and AI/ML workloads. 2) Data Modelling & Semantic Layer Define and maintain core business metrics and calculations. Ensure consistent definitions across all datasets and outputs. Develop and manage semantic models ...

relevant scientific, engineering, or data field. Demonstrated experience operationalizing MLOps, model lifecycle management, and validation in GxP contexts. Familiarity with generative AI, knowledge graphs, semantic layers, and digital twins for manufacturing and CMC. Experience leading global, multi-functional teams and vendor ecosystems across multiple time zones. Knowledge ...

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 ...

including testing, monitoring, lineage tracking, and documentation. Build secure and privacy‐conscious data practices, including access controls and appropriate handling of sensitive information. Design semantic layers and consistent business metrics to support analytics and decision‐making across the organization. Collaborate with Product, Growth, and Engineering teams on product analytics … working in B2C SaaS, subscription‐based, or marketplace environments. Familiarity with product analytics platforms such as Segment, Amplitude, Mixpanel, or similar tools. Experience designing semantic layers or canonical data models. Exposure to streaming technologies such as Kafka or Kinesis. Experience with ML infrastructure, feature stores, or AI‐related data ...