How You Will Spend Your Time

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)

• Apply software engineering best practices throughout: version control, CI/CD deployment, testing, and documentation as first-class deliverables

• 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

About You

• Strong dbt skills, writing clean, well-structured transformation models with clear business logic, documentation, and tests

• Strong SQL skills, including complex transformations and cross-system joins in Redshift or equivalent; proficient in Python and dbt Macros for data loading and transformation

• Solid understanding of dimensional modelling and semantic layer architecture, including staging, intermediate, mart, and semantic layers

• Experience with CI/CD deployment for data pipelines and applying software engineering best practices to analytics engineering workflows

• Experience with data quality and governance, including testing frameworks, lineage, column-level documentation, and deprecation discipline

• Experience with integration tooling (Fivetran, Hightouch, or equivalent) for maintaining source integrations alongside a Data Engineering team

• Finance data literacy — comfortable working with GL codes, P&L structures, billing records, and payroll data in business terms, not just as raw fields. You don't need an accounting background but you should be able to pick up Finance concepts quickly and ask the right questions

• Comfortable working closely with Finance stakeholders to translate business requirements into technical implementations; this role requires as much Finance collaboration as it does engineering

• Strong documentation habits, treating dbt model docs, pipeline runbooks, and data catalog entries as core deliverables, not afterthoughts

• Curious and self-driven, with a strong appetite for continuously learning new techniques and tools to extract value from data

• 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

Highly Regarded

• Experience working in or alongside a Finance, Accounting, or Finance Systems team

• Familiarity with NetSuite, Workday, Zuora, HiBob, or similar ERP, payroll, and billing platforms

• Exposure to financial close processes, revenue recognition, or period-end reporting cycles

• Experience owning a domain-specific slice of a dbt stack alongside a broader analytics engineering function

• Experience building AI-ready semantic layers where downstream consumers include AI agents or LLM-based workflows

• Background in a high-growth SaaS environment

At SafetyCulture, we care about people and growing the team, through:

Flexible working arrangements, we encourage you to create the best work blend while working from your home and the local SafetyCulture office;
Access to professional and personal training and development opportunities; Hackathons, Workshops, Lunch & Learns;
We encourage involvement in the community, open source work, attending talks and events, and experimenting with new technologies;
In-house Culinary Crew serving up daily breakfast, lunch and snacks;
Wellbeing initiatives such as subsidised fitness programs, EAP services and generous parental leave policy;
Table tennis, board games, gym sessions, book club, and pet-friendly offices.