Analytics Engineer

🏢 Paveakatroveinformationtechnologies · 📍 San Francisco, CA & New York, NY 💰 $169,000 - $194,000 / yr

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Position Summary & Responsibilities

Who We Are

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 9,000+ companies unparalleled confidence in every compensation decision.

Top tier companies like OpenAI, McDonald’s, Instacart, Atlassian, Affirm, Ericsson, Synopsys, Stripe, Doordash, Fanatics, 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 80% 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 $175M in funding from leading investors like Andreessen Horowitz (a16z), Index Ventures, Y Combinator, Insight Partners, Atomico, Venture Partners, and Craft Ventures.

The Data Team @ Pave

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.

What You'll Do

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 i

🎯 Candidate Preparation Guide For Paveakatroveinformationtechnologies Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Measuring compensation data intelligence, enterprise SaaS compensation workflows, and secure data handling standards.

📋 Preparation Priorities (Key Areas to Clarify):

1. Treating compensation data carelessly without strict confidentiality protocols
2. Failing to understand pay equity, compliance, and labor market dynamics
3. Building rigid systems that cannot adapt to complex merit matrix configurations

✨ Recommended Role Alignment Hook:

I engineer intelligent compensation infrastructure that turns messy labor market data into clear, equitable, and strategic pay strategies.

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