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.
Role Purpose
To support the build out of the end-to-end data, analytics and data science capability for SafetyCulture Care, our insurance business. It turns raw data into the pipelines, models and insight that power underwriting, pricing, risk and operational decisions, starting hands-on as the foundations are laid.
Key Responsibilities
Support the delivery of the end-to-end data lifecycle - capture, storage, transformation, modelling, materialisation and insight. Establishing the foundations the insurance business runs on.
Be hands-on in the delivery of data assets and products across data capture, pipelines, reporting and analytical/statistical models. Collaborating with business and technical subject matter experts to deliver best in class assets and products.
Support the selection of, and development of a fit for purpose data architecture and tooling (cloud platform, warehouse, orchestration and business intelligence).
Support the delivery of a fit for purpose data governance and controls framework aligned to applicable regulation so data is trusted, compliant and audit-ready.
Translate underwriting, pricing, claims and operational questions into analysis and models that measurably improve decisions across the business.
Collaborate with business subject matter experts to build out the reporting, semantic layer and self-serve insight layer so leaders and operators across SafetyCulture Care can answer their own questions.
Embed artificial intelligence and machine learning (AI/ML) into core insurance workflows in partnership with actuarial, underwriting, product and engineering.
Support the growth of a geographically and diverse skilled data team across governance, engineering, analytics, data science and automation.
Required Skills & Experience
Technical Skills
Strong general insurance domain knowledge, ideally commercial insurance, workers' compensation or general insurance actuarial.
Deep knowledge of the full data lifecycle with experience hands on building data capture, data pipelines, reporting and statistical/ML models ideally end-to-end.
Knowledge of data and analysis languages: SQL, SAS, Python, R, PL/SQL, PG/PL, VBA or Java.
Uses AI regularly and appropriately to accelerate delivery utilisation across coding assistants for pipelines and queries, drafting documentation, speeding up analysis and prototyping models.
Can evaluate and integrate AI/ML capabilities into insurance workflows (e.g. risk scoring, document extraction, anomaly detection) and assess output for quality, bias and compliance.
Practical knowledge of business intelligence and reporting platforms such as Tableau, Power BI, SAS Visual Analytics, Qlik or Hex.
Practical knowledge of data platforms, warehouses and cloud tooling e.g. Snowflake, Databricks, dbt, Airflow, Postgres, Redshift, Microsoft SQL Server, Oracle or DB2, AWS, Google Cloud Platform or Azure and Git/GitHub.
Practical knowledge of data and risk regulation such as the Australian Privacy Principles (APP), GDPR, along with a practical knowledge of ASIC / APRA standards (e.g. CPS 230, CPG 234/235), and an understanding of SOC 2 and ISO 27001 and frameworks and controls that satisfy them.
Practical knowledge of governance, engineering, analytics/reporting, data science (AI/ML), architecture and automation.
Behavioural Skills
Sets the standard for responsible, well-governed AI/ML use within the data function as it grows.
Comfortable building from a blank page translates business needs into a sequenced plan and starts delivering without waiting for a perfect brief.
Self-directed, identifies what the business needs rather than waiting to be told.
Hands on mindset, happy to write the query and build a pipeline today, and to develop the team that takes that work on tomorrow.
Commercially focused is able to connect data to business outcomes across underwriting, pricing, claims and operations.
Open and direct communicator who gives and seeks honest feedback, and can explain technical trade-offs clearly to non-technical stakeholders.
Success Looks Like
After 6–12 months:
An implemented and documented end-to-end data foundation with data flowing reliably from capture through to trusted reporting, models and AI tools.
Underwriting, pricing or claims teams are making materially better decisions because of analysis or a model delivered.
Governance and risk controls are documented and stand up to internal and external scrutiny against the relevant regulatory standards.
Leaders across SafetyCulture Care can self-serve core metrics rather than waiting on ad-hoc requests.
Key Stakeholders
SafetyCulture Care leadership
Underwriting, pricing and actuarial teams
Claims and operations
Product & Engineering (broader SafetyCulture)
Risk, compliance and legal
What You Need to Know
Office/in-person: Sydney-based, hybrid 3 days per week in the Sydney office (firm expectation).
Team: builds and leads a mixed local and remote data team over time; no direct reports at the outset.
Travel: occasional, to connect with remote team members as the team grows.
Other: works with regulated data must be able to meet relevant data privacy and security obligations.
More than a job
Equity with high growth potential, and a competitive salary
Flexible working arrangements
Access to professional and personal training and development opportunities
Hackathons, Workshops, Lunch & Learns
In-house Culinary Crew serving up daily breakfast, lunch and snacks
Barista coffee machine, craft beer on tap, boutique wines and a range of non-alcoholic beverages
Wellbeing initiatives such as subsidised fitness programs, EAP services and generous parental leave policy
Quarterly celebrations and team events
On-site gym, table tennis, board games, books library, and pet-friendly offices