Senior Analytics Engineer

2 weeks ago

Melbourne, Victoria, Australia Tinybeans Full time $100,000 - $150,000 per year

Tinybeans (ASX: TNY) is a leading private photo-sharing app that connects families and transforms moments into lasting memories. We've been loved and trusted by millions of parents and families worldwide since our founding in 2012, as shown by our 150,000 5-star reviews across Apple App and Google Play stores, with users in almost every country.

With ambitious growth and innovation plans, we aim to enhance the beloved Tinybeans family photo-sharing app further. This role will play a crucial part in our journey.

Your Part in Our Mission

Reporting to the Director of Data Analytics, the Senior Analytics Engineer is responsible for turning raw data into accurate, trusted and well-documented metrics and datasets that drive decision-making across the company. This role owns the analytics layer of our data stack including our dbt models, testing framework and metric definitions, ensuring that performance data is consistent, reliable and easily consumed by business stakeholders.

The Senior Analytics Engineer partners closely with product, marketing and executive teams to define requirements and deliver accessible, business-ready outputs. They are the bridge between data engineering and data consumption, translating raw inputs into usable knowledge through modeling, documentation and stakeholder collaboration.

This role will work closely with a team based in Australia, North America and Vietnam.

What you'll be doing…

  • dbt ownership: Build and maintain core dbt models that define Tinybeans' key metrics supporting ongoing efforts in user acquisition, engagement and retention. Maintain tests, documentation and version control

  • Support growth, marketing and lifecycle: model attribution, connect product analytics to ad platforms, enable experiment tracking and build cohort and funnel dashboards

  • Attribution modeling: Develop and maintain reliable models that attribute user registrations and conversions to marketing campaigns and acquisition sources

  • Data documentation: Deliver clear, accessible documentation on metric definitions, data sources and modeling logic for stakeholders across product, marketing and executive functions

  • Collaboration with marketing & lifecycle teams: Deliver user-level traits, segments and behavioral signals that power targeted messaging campaigns and experiments

  • Query performance optimization: Maintain performant SQL in dbt models and support data warehouse optimization efforts in collaboration with data engineers

  • Stakeholder enablement: Provide business users with clear, tested datasets for reporting in Tableau and other tools

  • Quality and governance: Implement testing frameworks and data validations to ensure consistency and prevent metric drift or silent failures

  • Technical leadership: Define modeling standards, mentor team members and help set the direction of Tinybeans' analytics architecture

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