Manager, Data Analytics

SafetyCulture

Data Analytics Manager driving the build out of data, analytics, and data science for SafetyCulture Care business. Collaborating with teams to enhance underwriting, pricing, risk, and operational decisions.

Posted 6/18/2026full-timeSydney • 🇦🇺 AustraliaMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSAzureCloudGoogle Cloud PlatformJavaMS SQL ServerOraclePostgresPythonSQLTableauVBA

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.
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.

Benefits

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data lifecycledata capturedata pipelinesreportingstatistical modelsmachine learningartificial intelligenceSQLPythonbusiness intelligence

Soft Skills

collaborationself-directedcommunicationproblem-solvinghands-on mindsetcommercial focusresponsible AI usefeedbacktranslating business needsteam development

Certifications

ISO 27001SOC 2