Lead Technical Product Manager

Xero

Melbourne

2 days ago

Melbourne

2 days ago

Apply

Lead Technical Product Manager

Lead the modernization of Xero's marketing data platform and drive AI integration across data products. Bridging engineering and business needs, you'll dismantle data silos, replace legacy frameworks, and collaborate globally to deliver actionable requirements. Hybrid role based in Melbourne or East Coast Australia.

Apply

Hybrid

Full-time

Lead

Big Data Systems

Real-time Data Products

Salary

Not specified

Work Location

Melbourne, Victoria, Australia, AU

Work Model

Hybrid: remote flexibility with in-office connection, designated boost days in Melbourne office.

Employment Type

Full-time

Experience Level

Lead: deep technical familiarity with big data systems, autonomous operation, managing conflicting priorities.

Core Qualifications

Technical (Must-have)

big data systemsreal-time data productsdata architectureartificial intelligencedata scienceanalytics

Soft Skills

communicationautonomydecision-makingconflict managementcommercial mindsetcollaboration

Key Responsibilities

•Lead modernization of marketing data platform.
•Drive integration of AI across data products.
•Bridge complex engineering systems and business needs.
•Dismantle data silos and replace legacy frameworks.
•Collaborate with commercial stakeholders to translate business strategies into requirements.
•Balance long-term architectural transformation with incremental deliveries.
•Empower teams to make data-driven decisions.
•Navigate and upgrade a fifteen-year-old architectural framework.
•Unify fragmented data sources to establish a single source of truth.
•Partner with data engineering and applied science leads to embed AI capabilities.
•Outline and deliver immediate reporting enhancements with sales, marketing, and customer support units.

Lead Technical Product ManagerProduct ManagementMarketingData PlatformAIBig DataHybridMelbourneXeroSoftware Development

Key Responsibilities

•Lead modernization of marketing data platform.

•Drive integration of AI across data products.

•Bridge complex engineering systems and business needs.

•Dismantle data silos and replace legacy frameworks.

•Collaborate with commercial stakeholders to translate business strategies into requirements.

•Balance long-term architectural transformation with incremental deliveries.

•Empower teams to make data-driven decisions.

•Navigate and upgrade a fifteen-year-old architectural framework.

•Unify fragmented data sources to establish a single source of truth.

•Partner with data engineering and applied science leads to embed AI capabilities.

•Outline and deliver immediate reporting enhancements with sales, marketing, and customer support units.