Staff, Analytics Engineer, GTM Data Science & Analytics

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table!

Working at Twilio offers many benefits, including competitive pay, generous time off, ample parental and wellness leave, healthcare, a retirement savings program, and much more. Offerings vary by location.

*Please note this role is open to candidates outside of California, Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, New Jersey, New York, Vermont, Washington D.C., and Washington State. The information below is provided for candidates hired in those locations only.

The estimated pay ranges for this role are as follows:

Based in Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, Vermont or Washington D.C. : $155,520 - $194,400.

Based in New York, New Jersey, Washington State, or California (outside of the San Francisco Bay area): $164,640 - $205,800.

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.

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 and dashboards.

Mentor and guide members of the data science and analytics team, establishing and enforcing best practices around data modeling, testing, documentation, and code review.