Contract Senior Data Analyst (SQL,Telemetry,Browser Engine)

About Mozilla

The Gecko platform is the core engine of our product experience.

To maintain competitiveness and user satisfaction, we must move beyond surface-level metrics to understand the deep correlations between engine performance and real-world user behavior.
We currently face challenges with data quality, such as bot contamination in nightly telemetry, which obscures our understanding of true performance trends.
A dedicated analyst is required to bridge the gap between engineering telemetry and actionable product strategy, ensuring that leadership has high-fidelity data to prioritize performance guardrails and technical budgets.

Create and maintain high-level overview dashboards that translate complex performance data into consumable formats for Product Managers and leadership.
Audit the Gecko codebase to identify missing collection points or "probes" necessary for a complete understanding of the product experience.
Develop methodologies and tools to identify and filter data inconsistencies, specifically focusing on isolating bot traffic and hardware-specific noise from telemetry streams.
Deliver deep-dive analyses correlating technical performance shifts (e.g., LCP, eTLD+1) with user-facing pain points and engagement trends.
Scope, Skills, and Effort Estimate
The analyst will focus exclusively on Gecko engine telemetry across multiple platforms (Desktop and Android), collaborating with the Performance Team, Data Team, and Product Leadership.

Advanced SQL (BigQuery/Redash), proficiency in data visualization using AI tooling, and experience with statistical analysis (e.g., Pearson’s correlation, multivariate analysis). Knowledge of Rust/C++ is a strong plus. Strong proficiency with AI tools like Claude Code, Codex, or similar is highly desirable.
Ability to understand browser component interactions and their impact on user experience.
Proven ability to work independently, translating technical requirements into business insights.
Comfortable working in unfamiliar technical problem spaces and figuring out a path forward with minimal guidance, using documentation, experimentation, source code, data, and other available resources