Finance Analytics Engineer

Why join us? We’re a global tech company,  just not the kind you’re picturing. Sure, we’ve got catered lunches, team events, cool merch, and yes... dogs in the office. But that’s not why people join. Our team of nearly a thousand people wakes up every day to make our product and our customers’ lives better. At SafetyCulture, you’ll hear “yes, let’s give it a shot” more often than “that’s not how we do things here.” People join because we’re building tools that make work better for the 3 billion people who keep the world moving - factory floor operators, baggage handlers, truck drivers, servers, store assistants. The ones who make things happen. We’ve got the scale and innovation you’d expect from big tech. The difference? No endless layers of sign-off. No corporate theatre. Just smart, experienced people solving real problems fast . The scale is big. But the ownership’s personal. Every full-time team member gets equity - real skin in the game. When we grow, you do too. We’re not perfect, no company is. But this next chapter of our growth is about scaling with intelligence, not just size - fueled by operational maturity, a clear vision, and a strong focus on AI.  This is big tech impact, without the big tech ick. If that excites you more than it scares you, you’ll fit right in. The Role 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. 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 Highly Regarded At SafetyCulture, we care about people and growing the team, through: