Analytics Engineering Manager, Core Data Science & AI

Pinterest; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Pinterest is seeking an Analytics Engineering Manager II, Core Data Science to elevate the quality and velocity of data science across the company. You will build robust data foundations, lead a diverse analytics engineering team, and partner closely with our Core Data Science organization to drive impactful insights.

You will shape people-facing and business-facing products by developing scalable tooling, instrumentation, and pipelines, while applying AI to accelerate analysis and maintain

Qualifications

• 6+ years of experience working with data in a fast-paced, data-driven environment, including experience managing engineers.
• Experience conducting workflow orchestration and ETL/ELT with large, complex datasets and DAG dependencies.
• Demonstrated ability to translate open-ended goals into defined, impactful objectives.
• High integrity and ownership, responsible for data sensitivity and final decisions.

Responsibilities

• Shape the future of Pinterest's people-facing and business-facing products by building rock-solid data foundations and tooling.
• Lead a high-performance analytics engineering team and partner with Core Data Science.
• Develop best practices for instrumentation and experimentation and communicate them to product engineering teams.
• Build and prototype AI tools and analysis pipelines to provide scalable insights and knowledge of data structures.

Skills

Analytics engineering

Leadership

SQL

Python

R

ETL/ELT

Data analysis

Education

Bachelor’s/Master’s/PhD in a quantitative field

Job description

Pinterest is seeking an Analytics Engineering Manager II, Core Data Science to elevate the quality and velocity of data science across the company. You will build robust data foundations, lead a diverse analytics engineering team, and partner closely with our Core Data Science organization to drive impactful insights.

You will shape people-facing and business-facing products by developing scalable tooling, instrumentation, and pipelines, while applying AI to accelerate analysis and maintain