Staff Data Engineer, Analytics

Discord•San Francisco, CA

About The Position

Discord is looking for a seasoned technical leader to join our Data team as a Staff Data Engineer, Analytics. You will drive technical vision and strategy for our analytical data infrastructure, owning the transformation and semantic layer that powers clean, tested, well-documented datasets that the company can trust and self-serve from. The data you build won't just inform internal decisions, it will underpin the metrics we share with investors, partners, and the public, requiring exceptional rigor, auditability, and precision. This role works closely with Data Science, Product, Finance, and Engineering teams.

Requirements

7+ years of experience in analytics or data engineering with a strong focus on building curated, consumer-facing datasets
7+ years of experience in designing, developing, and maintaining robust data models from structured and unstructured sources
Expert-level SQL and strong Python skills, with solid fundamentals in version control and CI/CD
Proven experience implementing data quality audits, monitoring systems, and automated remediation for massive datasets
Experience building and owning executive-level dashboards and reports using BI tools (e.g., Looker, Tableau, or similar)
Strong business acumen and communication skills, comfortable translating ambiguous business questions into concrete metric definitions and explaining complex implementations to audiences from engineering peers to executive leadership
Track record of hands-on collaboration with Data Science, Finance, and Product teams

Nice To Haves

Passion for Discord or online communities
Experience building or contributing to a semantic layer or metrics store
Experience with modern analytics and data engineering tools and workflows (dbt, BigQuery, or similar)
Experience defining and governing metric standards across a data organization
Experience with reporting infrastructure subject to external audit or SOX compliance

Responsibilities

Define technical strategy and architectural direction for analytics data infrastructure, building and maintaining enterprise-scale curated datasets and data models
Own metric definitions end to end, from partnering with stakeholders to define what we measure, to designing and implementing data model in production, to surfacing metrics and dimensions in dashboards and reports
Design and build sophisticated data models and analytical frameworks using SQL, Python, and modern data stack technologies
Develop data quality frameworks, monitoring, anomaly detection, and alerting at massive scale, with governance, lineage tracking, and change management rigor appropriate for externally reported numbers
Drive adoption of consistent data modeling patterns, naming conventions, documentation norms, and metric governance standards across the data organization
Lead cross-functional technical initiatives across product verticals and mentor engineers to accelerate delivery and harden data systems
Navigate ambiguity and make sound technical decisions with incomplete information, balancing short-term delivery with long-term infrastructure investment