Discord

Overview

Discord is looking for an experienced and passionate Data Engineer, Analytics to join our data team! You will be owning the transformation and semantic layer that turns data into clean, tested, well-documented tables and dashboards that data scientists, product managers, and business stakeholders can trust and self-serve from.

About Discord

Discord has a highly engaged community of millions of daily active users who use the platform for many different reasons, but there’s one thing that nearly everyone does: play video games. Discord plays a uniquely important role in the future of gaming, and we are focused on making it easier and more fun for people to hang out before, during, and a

Requirements & Eligibility

• 3+ years of experience in analytics or data engineering with a strong focus on building curated, consumer-facing datasets
• 3+ years of experience in designing, developing, and maintaining robust data models from structured and unstructured sources
• 3+ years of experience writing accurate and effective code in SQL
• Experience with designing data model to power a variety of use cases, including experimentation
• Fluency in Python or another programming language, with solid fundamentals in version control and CI/CD practices
• Experience building and owning executive-level dashboards and reports using BI tools (e.g., Looker, Tableau, or similar)

Key Responsibilities

• Design, build, and maintain curated analytical datasets and data models that serve as canonical sources for metrics, dashboards, and analyses
• Own metric definitions end to end, from partnering with data science and product team to define what we should measure, to implementing it in production, to surfacing it in visualization tools
• Build and maintain executive-level dashboards and self-serve reporting tools that enable business stakeholders to answer their own questions
• Partner closely with data science, product manager, and engineering teams to translate business questions into well modeled, performant, and discoverable data assets
• Establish and enforce data quality standards through testing frameworks, documentation, and monitoring for the datasets you own
• Drive adoption of consistent data modeling patterns, naming conventions, and documentation norms across the data organization