Senior Analytics Engineer

Twilio

9d•$141,520 - $208,000•Remote

About The Position

This position is needed to advance the consistency & quality of our R&D analytics data layer and accelerate the development velocity of analysts. Our Data Science and Analytics team seeks to empower R&D to make data-backed decisions that accelerate innovation and improve product performance. You will work closely within our team and across Product & Engineering to design and maintain a robust analytics data layer that enables trusted reporting on R&D metrics.

Requirements

4-5 years of professional experience in analytics engineering, data engineering, or a related discipline
Strong expertise in SQL and hands-on experience designing data models and orchestrating data pipelines using AWS Glue or similar technologies
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 owning ambiguous projects from beginning to end with minimal guidance
Strong technical communication and mentorship skills, with the ability to convey complex concepts to a range of audiences

Nice To Haves

5+ years of professional experience in analytics engineering, data engineering, business intelligence, or a related discipline–ideally in a B2B SaaS environment
Expertise in Python; distributed computing technologies like Hive, Presto, and Spark; and dashboarding tools like Looker or Tableau
Proven track record of implementing robust data quality and testing frameworks, including expertise with dbt tests, CI/CD, and data observability
Experience evangelizing and establishing data culture and best practices within a fast-paced technology organization

Responsibilities

Design and implement a formal analytics data layer using AWS Glue, Athena / Presto, and LookML
Collaborate within the Data Science & Analytics team and across Product & Engineering to define, document, and maintain alignment on metric definition and data lineage
Develop and maintain automated data reconciliation and quality checks to proactively identify and resolve discrepancies, ensuring accuracy and consistency of critical reports and dashboards
Lead investigations into complex data anomalies, conduct root cause analysis, and communicate findings and solutions effectively to both technical and non-technical audiences
Mentor and guide members of the data science and analytics team, establishing and enforcing best practices around data modeling, testing, documentation, and code review