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Job description
We are building an intelligence layer for the distributed energy industry.
Across our businesses, we operate in solar, battery storage, energy distribution, software, financing, installation workflows, after-sales services and energy assets. Together, these businesses generate significant operational data across customers, installers, suppliers, products, proposals, orders, projects, physical assets, financing and post-installation performance.
Our next challenge is not simply to centralise this data. It is to create a common digital representation of the distributed energy ecosystem: a shared Energy Ontology that enables our software, analytics systems and AI agents to understand how the real world is structured, how entities relate to one another, how they change over time and what actions can be taken.
We are looking for a Principal Knowledge Architect to lead this effort.
This is a rare opportunity to design a knowledge architecture from the ground up across a global, multi-business energy platform. You will have the opportunity to define the foundational model that shapes how data, software and AI operate across the group.
This is not a traditional Data Engineer, BI, Data Warehouse or Enterprise Architecture role. You will define the semantic and knowledge architecture that sits between our underlying data infrastructure and the AI applications, decision systems and operational workflows built on top of it.
What You’ll Do
Build the Energy Ontology
Define and continuously evolve the canonical ontology for the distributed energy ecosystem.
You will establish:
Core business objects and entity definitions
Attributes, properties and relationships
Events, states and lifecycle transitions
Actions and business rules
Identifiers and source-of-truth principles
Provenance and data lineage
Temporal and geospatial relationships
You will ensure that different businesses and systems describe the same real-world entities consistently.
Establish the Canonical Data Model
Work across multiple platforms and data sources to establish a shared representation of customers, installers, products, orders, projects, properties and energy assets.
Which systems and sources are authoritative
How entities are identified across platforms
How duplicate entities should be resolved
How information can be appropriately shared across businesses
How entities and relationships evolve over time
How common models can be used across software products without requiring every application to share the same physical database
Partner with the Global Data Platform Team
You will work closely with our global data and platform engineering teams, including our China-based Data Platform Team.
The Principal Knowledge Architect will own the ontology, semantic architecture, canonical object definitions, relationship models, knowledge architecture, data contracts, ontology governance, knowledge graph architecture and AI readiness of enterprise data.
Our Data Platform Team owns ingestion, pipelines, storage, transformation, APIs, infrastructure, data quality implementation, platform engineering and production systems.
Together, you will translate complex business reality into scalable production data infrastructure.
Build the Knowledge Layer for AI
Design the knowledge foundation that enables future AI systems to understand entities, relationships, state and operational context.
This could enable AI systems to understand:
Who a customer is across multiple interactions and businesses
Which installer services a customer or property
Which energy assets and products exist at a property
Which products and systems are compatible
Which proposals are most likely to convert
Which financing options may be appropriate
What has happened previously and what state an entity is currently in
What action a system should take next
The goal is to make enterprise data understandable and actionable for intelligent applications, not simply accessible.
Design Knowledge Graph & Entity Resolution Architecture
Develop the architecture required to connect fragmented records representing the same real-world entities.
This may include installers represented differently across multiple systems, customers interacting across different businesses, products appearing under different distributor SKUs, and installations connected to properties, products, installers, financing and performance records.
You will define approaches for:
Entity resolution and identity matching
Knowledge graph construction
Confidence scoring
Source-of-truth management
Data lineage and provenance
Ensure the Energy Ontology remains a governed, reusable enterprise capability rather than becoming another uncontrolled schema.
Creating and modifying objects
Versioning and backwards compatibility
Ownership and permissions
Naming conventions
Documentation
Ontology review and approval
You will ensure the ontology remains reusable across businesses and is not optimised for one application at the expense of the wider platform.
What We’re Looking For
You will bring 8+ years of relevant experience across knowledge architecture, semantic data modelling, data architecture, data platforms or related disciplines.
We are particularly interested in candidates with meaningful experience across several of the following areas:
Knowledge graphs
Canonical enterprise data models
Entity resolution and identity matching
Product or customer knowledge graphs
Master Data Management
Metadata and taxonomy systems
Data contracts and governance
AI knowledge systems
You should be comfortable working across both technical and business domains and be able to translate complex real-world concepts into scalable data and knowledge structures.
Technical Foundation
You should have a strong understanding of:
SQL and Python
APIs
Relational and graph databases
Event-driven architectures
Data lineage and quality
Identity and entity resolution
Experience with technologies such as Neo4j, RDF, OWL, SPARQL, GraphQL, Kafka, Snowflake, Databricks , or equivalent technologies is valuable but not mandatory.
We care more about your ability to design the right conceptual and knowledge architecture than your attachment to any specific technology.
Particularly Relevant Backgrounds
You may have worked in complex, data-rich platform environments involving large-scale knowledge, identity, marketplace, workflow, data or operational intelligence systems.
Experience in environments similar in complexity to Palantir, Amazon, Google, Microsoft, Salesforce, ServiceNow, Uber or Airbnb may be particularly relevant, but we are equally interested in candidates from other organisations who have solved comparable problems at significant scale.
What matters is your ability to create shared models of real-world entities, connect fragmented systems and make enterprise data usable for intelligent applications and operational decision-making.
What This Role Is Not
This is not primarily a:
Dashboard or BI role
Data warehouse or ETL role
LLM prompt engineering or chatbot role
ERP implementation role
Pure research ontology role
We are looking for someone who can connect business reality, ontology, data, AI, decisions and actions and translate that architecture into practical systems used across a growing global business.
If you are excited by the challenge of creating the knowledge foundation that allows data, software and AI to understand and act on the distributed energy ecosystem, we would like to hear from you.
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Worksite address
palo alto, CA, 94306, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.