Planet is a global provider of integrated technology and payments solutions for retail and hospitality customers.
We create great experiences for the millions of people who use our payments, software, and tax-free solutions every minute of every day.
Planet empowers its customers to deliver great customer experiences by combining payments and software in ways that drive greater loyalty, increase revenue and save time.
Founded over 35 years ago and with our headquarters in London, today we have more than 2,500 employees located across six continents serving our customers in more than 120 markets.
The Enterprise Data Architect is responsible for defining, governing, and evolving the organisation’s enterprise data model across all systems, including enterprise SaaS platforms (e.g. Salesforce, Workday) and internally developed systems.
The role provides architectural authority over how data is structured, captured, transformed, and consumed across the enterprise (from system of origin through to canonical and consumption layers) ensuring consistency, integrity, and alignment across operational and analytical domains
This role is critical to establishing a single, coherent representation of key business entities and enabling a transition from system‑driven to data‑driven operations.
Merchant hierarchy is the first priority domain and proving ground for this role; the mandate extends well beyond it to all core enterprise entities — transaction, financial, and pricing/billing — acting as the data architect for fundamental data designs across Planet’s technology stacks.
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Define and maintain enterprise-wide canonical data models for core domains (e.g. merchant, transaction, financial entities, pricing/billing)
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Establish consistent definitions, relationships, and semantics across all systems
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Own enterprise data dictionaries, entity definitions, and modelling standards
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Align canonical entity boundaries to the enterprise Target Architecture Model (TAM) capability model
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Lead the structured evolution of the enterprise data model
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Define and govern data structures within enterprise SaaS systems (e.g. Salesforce, Workday, internal systems)
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Ensure system-of-capture models align with enterprise canonical definitions
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Review and approve structural changes to key data objects (e.g. accounts, contracts, employees)
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Prevent divergence into system-specific data silos
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Establish clear boundaries between:
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System-of-capture models
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Canonical models
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Consumption models
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Act as the governing authority for enterprise data model changes across all systems
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Review and approve schema, integration, and transformation changes
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Define and enforce enterprise data standards, naming conventions, and modelling principles
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Ensure governance is consistently applied across SaaS, operational, and data platform layers, in partnership with the Data team who own platform-level data governance
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Ensure alignment between SaaS systems, operational data stores, and the data platform
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Validate transformations into canonical models
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Assess the enterprise impact of system changes (including SaaS platforms)
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Identify and resolve inconsistencies across systems
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Define how data flows across enterprise layers:
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Source systems (e.g. Salesforce, Workday)
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Canonical data layer (Snowflake)
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Consumption layer (reporting, APIs, operational services)
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Ensure clear separation between capture, canonicalization, and consumption
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Partner with the Data team, who operate and govern the Snowflake platform, to ensure governed, scalable data processing
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Ensure canonical models are consistently implemented within Snowflake
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This aligns with the enterprise model where Snowflake acts as the central, governed data layer, operated by the Data team, with canonical model authority held by this role.
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Assess impact of system and data model changes across the enterprise
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Define migration approaches from legacy structures to canonical models
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Support transformation programmes (e.g. merchant hierarchy, transaction model, pricing & billing)
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Ensure data architecture supports cloud and platform evolution
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Work with: Architecture teams, Data platform teams, Engineering teams, SaaS platform owners (Salesforce, Workday, etc.).
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Business data owners
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Translate business requirements into scalable data models
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Resolve cross-functional disagreements on data definitions
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Ensure enterprise data is structured and managed in alignment with security, privacy, and regulatory requirements across all systems
Define and enforce data security principles across:
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Source systems (e.g. Salesforce, Workday)
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Canonical data platform (Snowflake)
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Consumption layers (reporting, APIs, operational services)
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Ensure appropriate classification and handling of sensitive data (e.g. personal, financial, PCI)
Embed security into data architecture by design, including:
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Segregation of sensitive data domains
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Controlled access patterns and least-privilege principles
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Clear data ownership and accountability
Work with: Security and Risk teams, Compliance and Regulatory functions, Data platform teams, SaaS platform owners.
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Ensure alignment with enterprise information security policies and regulatory obligations
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Ensure the data platform (Snowflake) operates as a governed and compliant data store, supporting auditability, lineage, and controlled access
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Govern how data moves across systems, ensuring protection in transit and at rest and preventing uncontrolled duplication of sensitive data
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Integrity and consistency of enterprise data models
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Alignment between SaaS platforms, operational systems, and Snowflake
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Approval of material data model changes (approval gate on structural change)
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Definition and enforcement of enterprise data standards
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Delivery and adoption of canonical models across priority domains
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Enterprise data modelling (conceptual, logical, canonical)
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Data architecture across distributed systems and SaaS platforms
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Strong understanding of operational systems, operational data stores, and data platforms
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Canonical modelling and semantic layer design
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Data governance frameworks
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Cloud data platforms (e.g. Snowflake, MS Fabric)
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Data pipelines (batch and event-driven streaming e.g. Fivetran, Estuary, Kafka)
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SQL Database platforms (MS SQL, MySQL, PostgreSQL)
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NoSQL Database platforms (KV/Document/Graph)
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Vector Database platforms (to support AI)
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NewSQL (transaction horizontal write)
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API-driven (REST) and event-driven architectures (Kafka)
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Data quality, lineage, and metadata management
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Integration across heterogeneous systems
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Strong architectural authority and decision-making capability
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Ability to influence without direct system ownership
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Operates across business and technical domains
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Pragmatic, delivery-focused, outcome-driven
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The authority on enterprise data structure and semantics across all systems (including SaaS)
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Responsible for ensuring all systems align to a single enterprise data model
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The owner of canonical models and enterprise standards
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The owner of SaaS platforms themselves
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A data engineering or pipeline delivery role
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The owner of the Snowflake platform or data-governance operations — those sit with the Data team; this role governs the canonical model, the Data team operates and governs the platform
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A system-specific data steward (e.g. Salesforce) — this role sets enterprise canonical standards; stewards own data quality within their platform
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A full Chief Data Officer (CDO)
Planet is an equal opportunity employer where diversity is valued, and all employment is decided based on qualifications, merit, and business need.
Come and grow your career in the most exciting, fast paced technology market, with a business that delivers feel-good connected commerce.
We would love to hear from you – Apply now.
At Planet, we embrace a hybrid work model, with three days a week in the office.
Reasonable accommodations may be made in order to allow for an individual to perform the essential functions of this role successfully.
