Senior Product Analytics Engineer - Ads

Faire•San Francisco, CA

8d•Hybrid

About The Position

Ads is one of the fastest-growing and most strategically important parts of Faire's business, and the Ads Data team — now three years old — is entering a period of hypergrowth. As a product analytical engineer, you will own the end-to-end ads data foundation and models. You will partner closely with cross-functional teams in Product, Engineering, ML Applied Sciences, and Strategy & Analytics (S&A) to support product launches and roadmaps by designing and building data capabilities that inform and drive insights. You will lead the path in using AI to boost productivity and transform how analytical engineering operates—leveraging AI to streamline data pipeline development and operations, improve data consumption workflows, and automate routine analytical tasks. In this role, you'll see a direct link between your work, company growth, and user satisfaction. Join us and be part of a team that values data-driven innovation and excellence.

Requirements

4+ years experience in analytics & 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 with demonstrable competencies in designing well-architected data models and optimizing query performance.
Deep experience and knowledge of data warehousing concepts, ETLs, big data technologies, and analytics platforms.
Experience with Airflow, Docker, DBT or similar analytics workflow tools.
Bachelor’s or Master’s degree in Computer Science, Math, Physics, or a related technical field.

Nice To Haves

Preferred experiences within Ads domain.

Responsibilities

Lead the design, implementmentation, and optimization of end-to-end and scalable data pipelines and models for a domain defining the architecture, feature requirements, roadmap.
Collaborate with engineers, product managers, S&A, 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.
Redefine how we build, maintain, and scale data systems by embedding AI into our daily workflows.
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 with enthusiasm.