Original listing text, shown exactly as published by the company.
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…
