APAC Data Platforms - Tech Lead

Provides expert level IT technical lead services, including the direction, evaluation, selection, configuration, implementation, and integration of new and existing technologies and tools.

More than 45 years ago, John C. Bogle had a vision to start an investment company that did things differently. A company with no external shareholders. Where all the profits were invested back into the business and used to lower costs. Evidently, it was as bold as it was brilliant. To this day, Vanguard Group still has no external shareholders. That means no share prices to protect, and no profits to generate for outside owners.

Today, Vanguard is one of the world's largest investment management companies, serving more than 50 million investors worldwide. For more than 25 years Vanguard Australia has been supporting individual investors, financial advisers, and superannuation members to achieve their long-term financial goals.

This hybrid role (in office Tues-Wed-Thurs) is based in Melbourne, VIC

Join Vanguard's growing Technology division in Australia. Our Chief Data & Analytics Office's Data Platforms team is transforming how Australians invest by delivering timely, modelled, and AI-ready datasets that customers trust and understand.

What You'll Do

• Own BAU operations for APAC data platforms, ensuring stability and driving continuous improvement.
• Support the design, build, and rollout of a metadata-driven, incrementally loading data lakehouse solution on AWS using Databricks-and eventually own it.
• Champion Databricks adoption, guiding architecture decisions, best practices, and migration pathways.
• Collaborate globally with teams in Australia, Europe, and the US to deliver accessible, high-quality data.
• Drive governance and quality through maintainable, well-documented, resilient code aligned with DataOps principles.
• Mentor and uplift engineering capability across the team.
• Partner with leadership to prioritise initiatives and deliver measurable business value.

• Proven leadership in cloud-based data engineering teams, ideally in large-scale AWS environments.
• Expertise in Databricks and Spark (PySpark, SparkSQL), plus strong Python and SQL skills.
• Deep understanding of data modelling (dimensional modelling; Data Vault a plus).
• Strong communicator who can translate technical concepts into business language.
• Experience managing priorities across multiple time zones and collaborating globally.
• Solid grasp of Git, CI/CD, and modern engineering practices (DevOps, DataOps, CloudOps).
• Familiarity with ETL frameworks, data lakehouses, warehouses, and governance processes.
• Comfortable with ambiguity, proactive, and adaptable with strong learning agility.

• Financial services or investment management experience.
• Experience designing metadata-driven, incremental-load architectures on AWS.
• Knowledge of SaaS platforms, CRM/ERP systems, and diverse data formats (APIs, JSON, RDBMS).
• Exposure to data visualisation tools (Tableau preferred).