SafetyCulture's Finance function is building an AI-powered operating model, automating the mechanical, repeatable work across FP&A, Treasury, Accounting, Tax, AR, AP, Legal operations, and beyond so the team can focus on judgement, analysis, and the decisions that move the business.
We're looking for a Finance Analytics Engineer to own the data foundation that makes this possible. Where our Data Engineering team ensures data flows reliably into Redshift, you turn those raw sources into a trustworthy, governed, AI-ready Finance semantic layer that Finance workflows and AI agents can build on. That means owning the dbt transformation layer for all Finance source systems, encoding the business logic that makes Finance data meaningful, and putting in place the documentation, testing, and governance standards that make it reliable.
This role sits embedded in the Finance team. You'll work closely with Finance stakeholders to translate business requirements into dbt models, understanding not just what the data is but what it means in Finance terms. You'll also work alongside our Analytics Engineering and Data Engineering teams as a peer, aligning on warehouse conventions and shared dimension tables while maintaining clear ownership of the Finance data domain.
Build and own the Finance semantic layer
Design, build, and maintain the dbt models that power Finance workflows and AI agents, covering staging, intermediate, mart, and semantic layers for Finance source systems (NetSuite, Workday, Zuora, HiBob, banking feeds, and others)
Use SQL and Python/Macro for efficient data loading and transformation across the Finance data layer
Business logic and Finance collaboration
Work closely with Finance stakeholders to understand and encode the business rules that make Finance data meaningful: GL code to P&L line mapping, GL to balance sheet category, Workday forecast version logic, Zuora and Chargify deferred revenue reconciliation, HiBob to cost centre joins, and other Finance-specific transformations
Validate outputs against known Finance benchmarks to ensure correctness before models go into production
Security, access governance, and audit trails
Design and implement role-based access control for the Finance data layer, defining permission tiers (full Finance access, payroll-restricted access, department-level views) and managing service accounts for Claude and other agents
Partner with IT and Engineering to ensure the Finance data layer meets SafetyCulture's broader security and governance standards
Data quality, monitoring, and documentation
Implement automated data quality checks across Finance models, covering feed timeliness, format validation, reconciliation checks, and variance thresholds
Maintain documentation for every dbt model and pipeline, including field-level definitions in business terms, known limitations, freshness requirements, and runbooks, so the layer can be maintained and extended by others
Partner with Data Engineering and downstream consumers
Partner with the Data Engineering team on the staging layer contract, ensuring raw Finance source data lands in Redshift reliably and the handoff into the AE layer is clean
Consume shared dimension tables (ARR, org data) from the existing analytics engineering stack rather than rebuilding them
Make the Finance semantic layer queryable and reliable for downstream consumers including Finance team members, Claude skills, and AI agents
Strong dbt skills, writing clean, well-structured transformation models with clear business logic, documentation, and tests
Comfortable working independently and finding answers without being directed; able to navigate ambiguity and adapt quickly in a fast-paced environment
Clear communicator, able to work effectively across Finance, Analytics Engineering, and Data Engineering teams
Experience working in or alongside a Finance, Accounting, or Finance Systems team
Background in a high-growth SaaS environment
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