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

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.

We’re committed to building inclusive teams and cultivating a sense of belonging so our people can bring their whole authentic selves to work each day. We seek to make reasonable adjustments throughout our recruitment process to create an even playing field for all candidates. Thanks to the tireless efforts of the entire SafetyCulture team we’ve built an incredible culture which has seen us recognised as a Best Place to Work in Australia, the US and the UK .

Even if you don't meet every requirement listed in the ad, please consider applying for this role. We prioritise inclusion and value individuals with potential over a checklist of qualifications. Don't rule yourself out, hit that apply button if this job resonates with you.

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To all recruitment agencies, we do not accept resumes or partnership opportunities. Please do not forward resumes to SafetyCulture or any of our employees. We are not responsible for any fees associated with unsolicited resumes.