Staff, Analytics Engineer

Twilio•,

3d•$155,520 - $228,700•Remote

About The Position

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.

Requirements

Minimum 8 years of professional experience in analytics engineering, data engineering, business intelligence, or a related discipline, with a preference for direct experience supporting GTM or sales analytics in a SaaS environment.
Advanced expertise in SQL and hands-on experience designing data models and orchestrating data pipelines using dbt.
Demonstrated ability to partner with cross-functional stakeholders to codify, document, and reconcile critical business metrics, ensuring company-wide data alignment.
Proven track record of implementing robust data quality and testing frameworks, including expertise with dbt tests, CI/CD, and data observability.
Strong technical communication and mentorship skills, with the ability to convey complex concepts to a range of audiences.

Responsibilities

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.
Lead investigations into complex data anomalies, conduct root cause analysis, and communicate findings and solutions effectively to both technical and non-technical audiences.