Data Architect / Engineering Lead, Rotageek

• Deep, hands-on experience in data modelling for analytics, including dimensional modelling, facts, conformed dimensions, stated grain, slowly changing dimensions and entity resolution
• Real ownership of a curated data layer that other people use and trust
• Strong warehouse architecture experience, including layered models from raw through to staged and curated data, plus a governed semantic layer
• Strong SQL and experience with dbt or an equivalent transformation and modelling framework
• Sound judgement about which logic belongs in the warehouse, BI layer or application
• Experience designing data contracts with Engineering, covering schema, semantics, ownership and freshness
• Practical experience with multi-tenant analytics, row-level or attribute-level security, PII classification and GDPR-sensitive data
• Experience treating analytics as software, including Git, CI/CD, automated testing, environment parity, observability, SLOs and cost awareness
• Functional or discipline leadership experience, setting standards, raising the technical bar and mentoring without relying on formal line management
• The ability to explain complex data and architecture decisions clearly to technical and non-technical audiences
• You don’t need experience with every platform listed above. We’re more interested in your modelling depth, architectural judgement, ability to create shared standards and enthusiasm for building data products that people can trust
• You think in grains, conformed dimensions and semantic layers, modelling with intent rather than reflex
• You treat data as a production system, with the operational rigour you would expect from a critical application
• You define the approach clearly, make trade-offs visible and keep the architecture understandable to the people who build on it
• You create leverage through standards, documentation and self-service, so analysts, engineers and AI tools can work effectively without constant intervention
• You are business-minded, connecting the path from question to model and pushing back on complexity that does not serve the outcome
• You are conscious of security, cost and data quality from the start, rather than treating them as later hardening work
• You mentor through the work, share knowledge generously and turn mistakes into learning through blameless review

Desirable

• Our customers and teams need data they can trust. As Data Architect / Engineering Lead, you’ll help turn a growing analytics estate into a clear, governed and scalable foundation for better decisions, better products and trustworthy AI-powered experiences
• Data modelling is at the heart of this role. You’ll shape the layered warehouse model, define the grains and conformed dimensions, and design the semantic layer that connects real business questions to reliable answers
• You’ll own the architecture end to end, including data contracts with Engineering, multi-tenant security, deployment as code and the operational health of the estate. You’ll work closely with Engineering leadership, our global team in Australia and the wider Product function
• This is a senior technical role with functional leadership responsibility. You’ll set standards, mentor others and represent Data in cross-functional decisions, without needing to manage a large team. Your impact will show in an estate that scales, a discipline that shares knowledge and models that other people can confidently build on
• Own the analytics data architecture end to end, including layers, modelling conventions, data contracts and the governed semantic layer
• Build the foundational curated layer yourself, creating dimensional models with clear grain, conformed dimensions and the flexibility to answer cross-subject business questions
• Establish and maintain data contracts with Engineering, helping the organisation manage source changes without silently breaking downstream data
• Define how data is modelled across the warehouse, BI and application layers, keeping business logic in the right place
• Own multi-tenant security and the operational health of the estate, including tenant isolation, row-level security, PII handling, freshness, quality and cost
• Make deployment predictable through Git-based development, CI/CD for the warehouse and semantic layer, automated provisioning and consistent environments
• Set standards for testing, observability, backfills, quarantine and the day-to-day operation of analytics as software
• Lead the Data Engineering discipline through standards, technical direction, mentoring, hiring input and roadmap contribution
• Represent Data in architecture and cross-functional decisions, connecting the business question to the model that answers it
• Work with the group data function and the Australia team to keep the estate coherent across teams and data-residency boundaries
• Use AI to accelerate models, tests, documentation and schema discovery, while verifying the output wherever PII or a customer-facing number is involved
• The impact you’ll make
• Rotageek’s analytics estate is modelled, governed and easier to understand rather than growing through disconnected additions
• Business questions can be answered through a trusted semantic layer and well-designed curated models
• Source changes are managed through clear contracts instead of silently breaking downstream experiences
• Tenant isolation, PII handling and access controls are built into the architecture and applied consistently
• Warehouse and semantic-layer changes ship through CI/CD with useful testing, observability and environment parity
• Data freshness, quality and cost are visible and actively managed
• The Data Engineering discipline can build the next model to a shared standard without you needing to be in every conversation
• Natural-language analytics and AI-assisted experiences are grounded in governed, trustworthy data
• The tools and ways of working
• Dimensional modelling, layered warehouse design, stated grain, slowly changing dimensions, entity resolution and data contracts
• Snowflake preferred, or comparable cloud data warehouses such as BigQuery, Redshift or Databricks
• Dbt or an equivalent transformation and modelling framework
• A governed semantic or BI layer, such as dbt Semantic Layer, LookML, ThoughtSpot or a comparable platform
• Deep SQL and Python for data pipelines and supporting automation
• Git, CI/CD, environment parity, automated testing and observability for analytics as software
• Multi-tenant row-level security, PII masking, GDPR-aware access control, audit and retention
• AI-assisted tools such as Claude Code or Codex for models, tests and documentation, with disciplined review and validation

• We usually respond within three days
• Screening call: A virtual chat with one of our team to learn more about your experience, what excites you about the role and how you align with our people-first culture
• Technical and architecture interview: You’ll meet the hiring manager and explore your approach to data modelling, warehouse architecture, data contracts, security, delivery as software and functional leadership
• Final stage interview: You’ll meet members of the wider team and talk through how you would build trust in the data estate, support self-service and help the discipline grow