SafetyCulture - Finance Analytics Engineer

• 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) • Write tests that catch the failure modes that matter: uniqueness, referential integrity, business rule violations, and freshness • Ensure every model has a description, every column has a definition, and every metric has an owner. Documentation is part of done, not after • Name things clearly, version intentionally, deprecate explicitly. Lineage is visible and ownership is documented • 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 • Translate Finance requirements into dbt models that are accurate, well-documented, and maintainable, ensuring the logic is externally verifiable and not locked in anyone's head • 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 • Ensure audit logging is in place so the team can demonstrate who accessed what data and when, in any compliance or audit context • 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 • Build monitoring and alerting so data issues are detected before they affect Finance workflows or reporting • 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 • Manage and optimise data infrastructure at scale across the Finance domain, including Fivetran, Redshift, dbt, and Hightouch • 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