Staff, Analytics Engineer, GTM Data Science & Analytics

Who we are

At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences.

Our dedication to remote-first work , and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day

As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding Your career at Twilio is in your hands.

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Hiring and how we work

We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions!

Also, while we are a remote-first company, you may be asked to report in person on an ad-hoc basis for team gatherings, functional off-sites or customer meetings.

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See yourself at Twilio

Join the team as Twilio’s next Staff, Analytics Engineer, GTM Data Science & Analytics.

About the job

This position is needed to advance the quality, reliability, and strategic value of Twilio’s Go-To-Market (GTM) data by designing and maintaining a robust business data layer to enable trusted sales analytics, reporting, data science, & AI capabilities

The Staff, Analytics Engineer will play a mission-critical role in driving cross-functional alignment within GTM Data Science & Analytics and Sales Operations on key Sales & Finance business metrics, ensuring data consistency and empowering data-driven decision-making.

Responsibilities

In this role, you’ll

• Design and implement a formal business data layer in dbt, ensuring that data from multiple sources is centralized, reconciled, and serves as a single source of truth for GTM metrics.
• Collaborate with stakeholders within the GTM Operations team and Finance to define, document, and maintain alignment on key business metrics and data definitions. Develop and maintain automated data reconciliation and quality checks to proactively identify and resolve discrepancies, ensuring accuracy and consistency of critical reports