Analytics Engineer

Pave; San Francisco (CA); Hybrid; USD 169,000 - 222,000; Full time

Benefits offered by this job

Health insurance

Flexible PTO

Meal stipends

Education stipend

Parental leave

Commuter stipend

Job summary

Pave's Data team is building a real-time compensation platform to empower thousands of customers with accurate pay decisions. You will extend data models, design scalable pipelines, and own observability as our dataset grows.

We seek a data/analytics engineer with 4+ years of experience, strong dbt and Airflow skills, and familiarity with cloud data warehouses to ship impactful data products and support ML workflows.

Qualifications

• Product mindset and ownership of data infrastructure
• Ability to design scalable data pipelines
• Experience shipping data products or infrastructure that impact business outcomes
• 4+ years in a Data/Analytics Engineering role; product-facing preferred
• Exposure to ML workflows and collaboration with data scientists

Responsibilities

• Extend and maintain core data models powering compensation intelligence products
• Design scalable data pipelines across the product suite with emphasis on Market Data
• Own data observability: monitoring, testing, and validation to maintain trust as data scales
• Collaborate with data scientists, PMs and engineers to translate product needs into insights
• Help drive millions of dollars of revenue growth

Skills

Product Mindset

Scalability

Bias for Action

4+ years exp

Exposure to ML workflows

Track record of impact

Tools

dbt

Airflow

Job description

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, Affir, Ericsson, 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 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 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

What You'll Bring

• 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.
• Experience - 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

Compensation, It's What We Do.

At Pave, we believe compensation should be as thoughtful as the people we hire. Your total rewards package includes meaningful equity, best-in-class medical, dental, and vision coverage, unlimited PTO, and region-specific benefits designed around your life — not just your role. Your level and compensation are determined by your experience and how you show up throughout the interview process. We're always happy to walk you through how we think about leveling — just ask.

Targeted cash compensation for the role:

P3: $169,000 - $194,000

P4: $202,000 - $222,000

Benefits @ Pave

At Pave, growth isn't a perk — it's the point. As you develop, your role expands, your responsibilities deepen, and your compensation reflects the impact you're making.

What We Offer

• Your Health, Fully Covered: Comprehensive medical, dental, and vision coverage for you and your family, with a range of options designed to meet you where you are.
• Time That's Actually Yours: Flexible PTO and the freedom to work from anywhere in the world for up to a month — because life doesn't pause, and neither should you.
• Fuel for the Work: Lunch and dinner stipends plus fully stocked kitchens, so you can stay energized without thinking twice about it.
• Room to Keep Growing: A quarterly education stipend to invest in the skills and knowledge that matter most to you.
• Support When It Matters Most: Robust parental leave so you can be fully present for the moments that count.
• Getting Here, Made Easier: A commuter stipend to support the in-person collaboration that makes great work happen.

Life @ Pave

Founded in 2019 with a clear purpose and a team that has never wavered from it, Pave has grown into a global force in compensation management — giving thousands of companies the tools to take control, build confidence, and earn credibility in every pay decision they make. And we're just getting started. Headquartered in San Francisco's Financial District, with regional hubs in New York City's Flatiron District, Salt Lake City, Kraków (Poland), and the United Kingdom — wherever you're based, you'll find the same thing: people who genuinely care about the work, each other, and the customers that rely on Pave.

We run a hybrid culture that brings teams together in person 3 to 4 days a week — and every Friday, the whole company gathers for our Team Sync: breakfast, new hire welcomes, product updates, fireside chats, and yes, the occasional Kahoot. It's one of the things people notice when they join us — that we truly enjoy spending time together.

Our culture is shaped by five values we live every day:

• Be Intellectually Honest — Truth over comfort. We face reality clearly and speak directly, even when it's hard.
• Play to Win — We're not here to participate. We're here to be the #1 compensation platform in the world, and we act like it.
• Uphold the Pave Platinum Standard — We hold ourselves to the highest bar — for our customers, our data, and each other.
• One Team — We win and lose together. Titles don't drive decisions here — shared goals do.
• Hug of Jawn — Hard to define, impossible to miss. Ask your recruiter.

Our Vision:

Unlock a labor market built on trust.

Our Mission:

Build confidence in every compensation decision.

We build software that transforms how companies pay their people — and we believe the team behind that software deserves the same thoughtfulness. If you're ready to help shape the future of compensation alongside people who are smart, humble, and genuinely motivated by the problem we're solving, we'd love to meet you.