Senior Analytics Engineer - Marketplace

Faire•San Francisco, CA

7d•$196,000 - $269,500•Hybrid

About The Position

Faire is seeking a Senior Analytics Engineer to build data capabilities that support product launches and roadmaps for features serving small and medium businesses. The role works closely with Data Scientists, Product Analysts, and Software Engineers, with a focus on using data and AI to inform product decisions and improve how analytical engineering operates. The engineer will help streamline data pipeline development and operations, improve data consumption workflows, and automate routine analytical tasks. This technical leadership role involves building and supporting large-scale, high-performance data storage and processing systems, leading cross-functional projects, and developing scalable data models and pipelines. The position also focuses on data quality, system reliability, operational excellence, and security. Faire describes its work as having a direct connection to company growth, user satisfaction, and the experience of independent retailers and small businesses.

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.
Experience with Airflow, Docker, DBT, or similar analytics workflow tools.
BA/BS degree in Computer Science, Math, Physics, or a related technical field.

Responsibilities

Lead cross-team and multi-phase projects from design through implementation in collaboration with cross-functional partners.
Lead technical execution of scalable data models and pipelines for a domain, defining architecture, feature requirements, and roadmap.
Build data pipelines with rigorous data quality, validation, testing, and observability practices.
Embed AI in daily workflows to change how data systems are built, maintained, and scaled.
Optimize processes for operational excellence in project management and system reliability.
Ensure data systems and workflows meet security standards.
Promote data quality standards to support accurate and reliable insights.
Embrace emerging technologies.