Senior Product Analytics Engineer - Ads

Faire•San Francisco, CA

9d•$196,000 - $269,500•Hybrid

About The Position

Faire is a technology wholesale platform connecting independent retailers with products from around the world. The Ads Data team supports one of Faire’s fastest-growing and most strategically important business areas. In this role, you will own the end-to-end ads data foundation and models, partnering with Product, Engineering, ML Applied Sciences, and Strategy & Analytics to support product launches and roadmaps through data capabilities that drive insight and inform decisions. You will help shape how analytical engineering operates by using AI to streamline pipeline development and operations, improve data consumption workflows, and automate routine analytical tasks. The role combines analytics and data engineering expertise, with a focus on large-scale, high-performance data storage and processing systems, scalable data models, and reliable analytical workflows. The position is hybrid, with employees currently working in the office three days per week and eligible for up to four weeks of remote work per year.

Requirements

At least 4 years of experience in analytics and data engineering roles focused on data modeling, large-scale data processing, and tool development for analytics or data science use cases.
Demonstrated communication and leadership skills, with a history of initiating and steering successful projects across multiple stakeholders.
Production-level development experience in Python.
Strong SQL skills, including designing well-architected data models and optimizing query performance.
Deep experience and knowledge of data warehousing concepts, ETLs, big data technologies, and analytics platforms.
A bachelor’s or master’s degree in Computer Science, Math, Physics, or a related technical field.

Nice To Haves

Preferred experience within the Ads domain.
Experience with Airflow, Docker, DBT, or similar analytics workflow tools.

Responsibilities

Lead the design, implementation, and optimization of end-to-end, scalable data pipelines and models for a domain, defining its architecture, feature requirements, and roadmap.
Collaborate with engineers, product managers, Strategy & Analytics, and ML Applied Scientists to translate business requirements into production-grade data systems and insights.
Build and maintain data infrastructure, pipelines, and models that ensure data quality, consistency, and accessibility for analytical workflows.
Embed AI into daily workflows to redefine how data systems are built, maintained, and scaled.
Mentor team members on best practices for scalable data engineering and quantitative problem-solving.
Ensure and promote data quality standards for accurate and reliable insights.
Embrace emerging technologies.