Analytics Engineer in Melbourne, Washington at Blinq

Blinq is the AI contacts app for people who meet people.

We're on a mission to unlock the world's relationship potential. So much of what's possible in the world is locked inside relationships that never quite happen. A missed conversation, a forgotten follow-up, the right person at the wrong time. We're building so those chances aren't left to chance.

What started as the world's #1 digital business card is now used by 4 million professionals and at 93% of the Fortune 500. We've raised $45m+ from Blackbird, Square Peg, HubSpot Ventures, and Touring Capital.

Learn more: https://blinq.me/

• You want to be part of an AI-native company at an inflection point. Big enough to have real momentum, customers, and resources. Small enough that the work you do shapes where it goes from here.
• You want to do the best work of your life alongside people doing the same. We hold an unusually high bar for talent and we hire people who treat that as a feature, not a threat.
• You want to build something globally ambitious from a part of the world that doesn't always think this big. Our team is in Sydney, Melbourne, San Francisco, and New York, and we're betting that the next category-defining company in our space comes from Australia.
• You want to work in person, on purpose. Three days a week in the office, two wherever works for you. Creative energy and momentum compound face to face.

We're looking for an Analytics Engineer to turn Blinq's growing data into trusted, decision-ready insight across Product, Growth, Revenue, and Operations.

This is a high-impact role at the intersection of data engineering and analytics. You'll build the models, metrics, and self-serve foundations that help teams understand how people discover, use, and pay for Blinq. You'll work closely with stakeholders to define what matters, make data easier to use, and ensure decisions are grounded in consistent, reliable numbers. This is a hybrid role based in Melbourne or Sydney.

• Own and evolve Blinq's analytics layer, building clean, reusable data models that turn raw product and business data into trusted datasets.

• Partner with Product, Growth, Revenue, Finance, and Engineering to translate ambiguous questions into clear metrics, analysis, and data products.

• Build and maintain scalable ELT workflows using SQL, dbt, and modern cloud data tools.

• Define canonical metrics and semantic models so teams work from consistent definitions across dashboards and decision-making.

• Develop high-quality dashboards and self-serve reporting that help teams move quickly without relying on one-off analysis.

• Support product analytics and experimentation, including funnel analysis, cohorts, retention, activation, monetisation, and A/B testing.

• Improve data quality and trust through testing, documentation, lineage, monitoring, and clear ownership.

• Investigate complex questions, surface the “why” behind performance, and turn insights into practical recommendations.

• Work with data and software engineers to improve instrumentation, event tracking, and the reliability of data flowing from source systems.

• Raise the bar for how Blinq uses data by improving standards, tooling, and analytics practices as the company scales.

• 3+ years in analytics engineering, data analytics, BI, or data engineering, ideally in high-growth SaaS, with a high-ownership mindset and comfort when priorities change

• Advanced SQL and dbt skills, with experience designing maintainable data models and applying strong standards for testing, documentation, and data quality

• Experience with a cloud data warehouse such as BigQuery, Snowflake, or Redshift, with a strong understanding of ELT, dimensional modelling, and data governance

• Proven ability to define trusted metrics, build useful dashboards or self-serve reporting, and partner with stakeholders to turn ambiguous questions into clear analytical work

• Strong product and commercial instincts, with the ability to connect user behaviour, experimentation, and revenue outcomes, then explain the insights in a simple, practical way