Manager II, Engineering- Analytics Engineering, Core Data Science
About this role
Managing engineers, prototyping AI tools, and building robust data foundations form the core of daily operations for this Analytics Engineering Manager II. The team partners directly with the Core Data Science organization to improve data velocity, create scalable analysis pipelines, and establish best practices for instrumentation and experimentation across the platform. Pinterest relies on analytics engineers to shape people-facing and business-facing products by developing self-service datasets and driving data democratization.
Leadership involves guiding a diverse team of engineers while collaborating cross-functionally with product managers, researchers, and designers to turn insights into concrete actions. Candidates who thrive here possess a strong background in handling complex datasets, a proven ability to integrate AI into daily workflows for velocity and quality, and a track record of translating open-ended goals into structured, impactful objectives.
What you will do
- Build, inspire, and grow a high-performance, diverse team of analytics engineers while fostering an inclusive culture.
- Develop and communicate best practices for instrumentation and experimentation to product engineering teams.
- Build and prototype AI tools and analysis pipelines iteratively to deliver insights at scale.
- Collaborate with data scientists and engineers to create tools and processes enabling the self-service of key datasets and metrics.
- Contribute to comprehensive documentation and drive the strategy for better data quality and data democratization.
- Partner with cross-functional leadership, including product managers, engineers, designers, and researchers, to build new user experiences.
What you need
- Bring 6+ years of experience working with data in a fast-paced environment, including managing engineers.
- Demonstrate fluency in SQL or other database languages alongside a scripting language like Python or R for handling complex datasets.
- Possess experience with workflow orchestration and ETL/ELT involving large DAG data dependencies.
- Show a consistent track record of scoping open-ended goals into defined analytics solutions.
- Apply AI tools to improve workflow speed and quality while maintaining critical evaluation and verification.
- Hold a Bachelor's, Master's, or PhD in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline, or possess equivalent experience.
Nice to have
- Experience in automation, anomaly detection, or infrastructure and workflow optimization.
- Experience building internal tooling to maximize engineering velocity.
Benefits
- Equity eligibility
Skills and tech stack
Questions candidates ask
What is the remote work policy for this position?
The role can be situated anywhere in the country as part of the PinFlex working model. However, the position requires being in the office for in-person collaboration 1 to 2 times per month.
What is the compensation range for this role?
The base salary range for this position is $177,185 to $364,795 USD for US-based applicants. The position is also eligible for equity, with final compensation determined by factors such as location, travel, relevant prior experience, and particular skills.
What kind of experience is expected with AI tools?
Candidates must demonstrate an ability to use AI to improve speed and quality in day-to-day workflows, such as drafting pipelines, exploring approaches, and automating documentation. A strong track record of critical evaluation, testing, source-checking, and data validation is required to ensure correctness.