Top Data & Analytics Jobs in Melbourne, VIC

Develop and operationalize data and AI governance frameworks, translating policy and regulatory requirements into practical guidance. Partner cross-functionally to support responsible AI adoption, data stewardship, governance forums, literacy programs, and technical implementation. Advise stakeholders on bias, transparency, explainability, privacy, risk, and evolving regulations while improving governance processes and tooling.

Build and maintain SQL data models, dbt pipelines, snapshots, governed semantic layers, and trusted data products in Snowflake. Support financial, workforce, and legal reporting while helping develop conversational AI and agentic data capabilities. Partner with Finance, Legal, and People teams to translate business questions into reliable metrics and models. Validate outputs, improve data quality, share engineering patterns, mentor colleagues, and establish consistent modeling standards.

Design and build scalable data solutions spanning ingestion, modelling, pipelines, and platform infrastructure. Own complex technical problems, improve data quality and self-service capabilities, and operate production systems with monitoring and incident response. Use Snowflake, dbt, Prefect, AWS, Terraform, and Flink to develop reliable data platforms and real-time products. Collaborate with product, security, engineering, and data-consuming teams while promoting automation, testing, CI/CD, and modern engineering practices.

Lead a distributed data engineering team to design, build, and maintain core data models and real-time data flows on an AWS/Snowflake platform. Balance strategic architecture and hands-on delivery, manage risks and dependencies, establish SLOs and DataOps practices, and coach senior engineers to improve platform quality and performance.

Develop and implement data and AI governance frameworks, stewardship models, governance forums, and practical guidance. Partner with technical, regulatory, privacy, security, risk, and product teams to enable responsible data use and AI adoption. Deliver data and AI literacy programs, address bias, transparency, and explainability, and translate complex regulations into actionable governance practices. Support data quality, architecture, lineage, catalogues, and responsible AI controls across the organization.