Senior Analytics Engineer
New
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TwilioR&D analytics
Location: Remote - US; This role will be remote, but is not eligible to be hired in CA, CT, NJ, NY, PA, WA.Full-TimeSenior
SalaryBased in Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, Vermont or Washington D.C. : $141,520.00 -- $176,900.00; Based in New York, New Jersey, Washington State, or California (outside of the San Francisco Bay area): $149,840.00 - $187,300.00. Based in the San Francisco Bay area, California: $166,400.00 - $208,000.00
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Job Details
- Experience
- 4-5 years of professional experience in analytics engineering, data engineering, or a related discipline; 5+ years desired.
- Required Skills
- PythonSQLData modelingdbtLooker
Requirements
- Have 4-5 years of professional experience in analytics engineering, data engineering, or a related discipline.
- Demonstrate strong expertise in SQL.
- Have hands-on experience designing data models and orchestrating data pipelines using AWS Glue or similar technologies.
- Be able to partner with cross-functional stakeholders to codify, document, and reconcile critical business metrics.
- Have a track record of owning ambiguous projects from beginning to end with minimal guidance.
- Demonstrate technical communication and mentorship skills for audiences with varied levels of technical expertise.
- Desired: Have 5+ years of experience in analytics engineering, data engineering, business intelligence, or a related discipline, ideally in B2B SaaS.
- Desired: Bring expertise in Python, distributed computing technologies such as Hive, Presto, and Spark, and dashboarding tools such as Looker or Tableau.
- Desired: Have experience with data quality and testing frameworks, including dbt tests, CI/CD, and data observability.
- Desired: Have experience establishing data culture and best practices in a technology organization.
Responsibilities
- Design and implement an analytics data layer using AWS Glue, Athena/Presto, and LookML.
- Collaborate with Data Science & Analytics, Product, and Engineering to define and document metric definitions and data lineage.
- Develop and maintain automated data reconciliation and quality checks for reports and dashboards.
- Investigate complex data anomalies, perform root cause analysis, and communicate findings and solutions.
- Mentor and guide team members, establishing best practices for data modeling, testing, documentation, and code review.
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