Analytics Engineer

Pave•San Francisco, CA

1d•Hybrid

About The Position

At Pave, we're building the industry’s leading compensation platform, combining the world's largest real-time compensation dataset with deep expertise in AI and machine learning. Our platform is perfecting the art and science of pay to give 8,500+ companies unparalleled confidence in every compensation decision. Top tier companies like OpenAI, McDonald’s, Instacart, Atlassian, Synopsys, Stripe, Databricks, and Waymo use Pave, transforming every pay decision into a competitive advantage. $190+ billion in total compensation spend is managed in our workflows, and 70% of Forbes AI 50 use Pave to benchmark compensation. The future of pay is real-time & predictive, and we’re making it happen right now. We’ve raised $160M in funding from leading investors like Andreessen Horowitz, Index Ventures, Y Combinator, Bessemer Venture Partners, and Craft Ventures. As part of the Data team at Pave you will help us redefine how companies understand the labor market and determine compensation. Even the most innovative tech companies in the world often use spreadsheets full of flawed statistics to determine how to pay. At Pave we’ve built a system of real-time integrations that allow us to bring best practices from machine learning, data science, software tooling, and AI to an industry that is built on data, but doesn’t have the tools it needs to fully leverage it.

Requirements

Product Mindset - You want to be a core contributor in building and maintaining the data infrastructure for a product. You intuitively understand how decisions made within the data pipeline affect the user experience downstream.
Scalability - You design and implement systems that are robust and scalable, ensuring they can efficiently handle future growth and evolving use-cases.
Bias for Action - You’re a catalyst and an accelerator. You’re constantly unblocking yourself and others while making strategic trade-offs.
4+ years of experience in a Data/Analytics Engineering role, ideally in a product-facing capacity.
Proficiency with dbt and airflow, and familiarity with cloud data warehouses.
Exposure to ML workflows - you've collaborated with data scientists or machine learning engineers to transform features, create training data sets, and deploy and monitor models
Track record of impact - you've shipped data products or infrastructure that meaningfully improved business outcomes and end user experiences

Responsibilities

Extend and maintain core data models that power Pave's compensation intelligence products
Design scalable data pipelines that support production use cases across our product suite, with an emphasis on Market Data
Own data observability by implementing monitoring, testing, and validation frameworks that maintain trust in our dataset as it scales
Collaborate cross-functionally with data scientists, product managers and software engineers to translate product needs into insights that supported our thousands of customers
Help drive millions of dollars of revenue growth