ARE YOU READY TO EMBARK ON A CAREER THAT TRULY AFFECTS PEOPLE AROUND THE WORLD? TRULIOO INVITES YOU TO BE A CATALYST FOR CHANGE IN THE DYNAMIC REALM OF DIGITAL IDENTITY VERIFICATION. AS THE GLOBAL FRONT-RUNNER IN OUR INDUSTRY, WE ARE REDEFINING HOW BUSINESSES GROW, INNOVATE AND COMPLY ONLINE.

Picture yourself at the forefront of innovation, contributing to our award-winning platform that enables organizations worldwide to quickly onboard customers, optimize costs and combat fraud. Fueled by Silicon Valley support, Trulioo stands as the trusted platform that can verify more than 5 billion people and 700 million business entities spanning 195 countries.
But Trulioo is more than a tech company. We are a united force of dedicated experts committed to establishing trust online.
Headquartered in Vancouver and with strategic hubs in San Diego and Dublin, we foster a culture of collaboration and open communication. Our offices support a hybrid model and staff typically work three days per week at a hub location. Join us where excitement meets innovation and contribute to a world where trust and technology unite.

POSITION SUMMARY:

We’re looking for an Analytics Engineer to join the Data Science team at Trulioo. This is a technical and hands-on role focused on building tools, applications, and automation to supercharge analytics workflows and make data more accessible to stakeholders across the company.
You’ll work closely with data analysts, data operations engineers, and data engineering to develop scalable internal tools — from Python-based automation scripts to Slackbots for reporting access. If you enjoy turning recurring analytics requests into elegant codebases, building efficient systems, and making life easier for data teams and business stakeholders alike, this role is for you.

• Develop and update Python-based applications and tools to automate common analytics workflows and repetitive analyst tasks.
• Build and maintain internal APIs and command-line tools that allow stakeholders to request and access data more easily.
• Collaborate with analysts to identify automation opportunities and transform manual reporting into efficient, reusable programs.
• Partner with the Data Engineering and Data Operations teams to integrate tools with data warehouse layers and AWS services.
• Implement best practices for version control, testing, deployment, and documentation for all analytics tooling.
• Continuously monitor tool usage and performance, iterating to improve speed, reliability, and usability.
• Write advanced SQL queries to support tool functionality and troubleshoot data requests.