Job Description
Candidate evidence
Candidates should be prepared to present a sanitised example of work they personally led or substantially
contributed to, such as an enterprise conceptual model, business or domain ontology, taxonomy or cross-system
semantic harmonisation initiative. Experience limited to physical schemas, dimensional models or ERDs derived
from source systems is not sufficient.
Must-have experience
Demonstrable hands-on delivery of conceptual data models in complex enterprise environments.
Practical experience developing or applying business or domain ontologies, taxonomies or related knowledge-representation models.
Experience defining concepts independently of application schemas and vendor technologies.
Experience reconciling conflicting terminology and entity representations across systems or business
domains.
Strong understanding of identity, classification, relationships, cardinality, grain, time, lifecycle and business
rules.
Proven ability to facilitate decisions with business experts and architecture stakeholders and communicate clearly in English.
Ability to translate conceptual work into logical models and practical implementation guidance.
Nice-to-have experience
Energy, utilities, industrial infrastructure, asset management or service domains.
Modelling assets, equipment, telemetry, maintenance, documents, media evidence or AI-generated
observations.
Metadata management, business glossaries, lineage, provenance and data governance.
Knowledge graphs or standards such as RDF, RDFS, OWL, SKOS or SHACL.
Dimensional modelling, Data Vault, modern lakehouse patterns and open semantic specifications such as
Apache Ossie.
Awareness of Microsoft Azure, Fabric, Databricks, dbt and multi-region or multi-tenant data platforms.
Job Responsibilities
Key responsibilities<\/p>\n
Work with business experts and architects to define important concepts, terminology, classifications and
relationships.
Develop and maintain enterprise and domain conceptual data models, taxonomies and pragmatic business
ontologies.
Distinguish business concepts from source-system records, application objects, tables and APIs.
Reconcile conflicting terminology, identifiers and representations across SAP, Salesforce, IoT and other
systems.
Define entity identity, lifecycle, temporal meaning and relationships, including evidence, observations and
machine-derived assertions.
Translate conceptual models into logical models, data contracts and implementation guidance together with
data architects and engineers.
Maintain traceability from business definitions to source data and downstream data products.
Facilitate modelling workshops, document semantic decisions and establish practical modelling standards.
Expected deliverables
Enterprise and domain conceptual data models
Governed vocabulary, definitions and synonyms
Taxonomies, classification models and a pragmatic business or domain ontology
Entity identity, relationship, lifecycle and temporal definitions
Concept-to-source and concept-to-logical-model mappings
Modelling standards, semantic decision records and implementation guidance<\/p>\n
Department\/Project Description
Project context
The project is a long-term data platform initiative for the energy infrastructure domain. It will
integrate enterprise data from SAP, Salesforce and legacy systems with IoT telemetry, documents, images and
machine-derived observations. The platform will support analytics, applications and governed AI use cases
across multiple regions, tenants and cloud environments.<\/p>\n
Role purpose
Lead the development of the enterprise conceptual data model and supporting ontology. Establish consistent
business meaning across domains and source systems, then guide its translation into logical models, data
products and semantic definitions.<\/p>\n","protected":false},"excerpt":{"rendered":"
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