Analytics Engineer at Pave

San Francisco, California, United States

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Not SpecifiedCompensation

Mid-level (3 to 4 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, HR TechnologyIndustries

Requirements

Product Mindset - Intuitive understanding of how data pipeline decisions affect downstream user experience
Scalability - Ability to design and implement robust, scalable systems for future growth and evolving use cases
Bias for Action - Catalyst and accelerator who unblocks self and others while making strategic trade-offs
Experience - 4+ years in a Data/Analytics Engineering role, ideally product-facing; proficiency with dbt and Airflow; familiarity with cloud data warehouses
Exposure to ML workflows - Collaboration with data scientists or ML engineers to transform features, create training datasets, deploy, and monitor models
Track record of impact

Responsibilities

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

Skills

Key technologies and capabilities for this role

TypeScriptNode.jsReactGCPMachine LearningAI

Questions & Answers

Common questions about this position

What technologies does Pave use for engineering?

Pave uses TypeScript, Node.js, and React, hosted on GCP.

What are the main responsibilities of the Analytics Engineer role?

Responsibilities include extending and maintaining core data models for compensation intelligence products, designing scalable data pipelines with emphasis on Market Data, owning data observability through monitoring and validation frameworks, and collaborating cross-functionally with data scientists, product managers, and software engineers.

Is this role remote or does it require office work?

This information is not specified in the job description.

What is the salary or compensation for this position?

This information is not specified in the job description.

What kind of experience makes a strong candidate for this role?

Strong candidates are passionate engineers excited about building robust, data-rich systems that simplify complex compensation processes at scale, with skills in data modeling, scalable pipelines, data observability, and cross-functional collaboration.

Pave

Compensation management solutions for businesses

About Pave

Pave provides compensation management solutions designed for businesses of all sizes, particularly those backed by venture capital. Its main product is a suite of tools that integrate with existing HR Information Systems (HRIS), Applicant Tracking Systems (ATS), and Cap Table software, allowing companies to access real-time data for employee compensation planning and benchmarking. This integration helps eliminate the need for spreadsheets and manual data entry, enabling HR leaders to make informed decisions based on accurate information. Pave operates on a subscription-based model, offering various tiers that may include advanced analytics and personalized support. The company aims to simplify the compensation analysis process, making it easier for HR departments to ensure fair and competitive pay, which can lead to improved employee satisfaction and retention.

New York City, New YorkHeadquarters

2019Year Founded

$165.4MTotal Funding

SERIES_CCompany Stage

Data & Analytics, Enterprise SoftwareIndustries

1-10Employees

Benefits

Competitive salary

Equity

Medical, dental & vision coverage

Commuter benefits

Catered lunch

Unlimited PTO policy

Risks

Competition from established HR tech companies like Workday and ADP is intensifying.

Data privacy concerns may arise from integrating with multiple HR systems.

Economic downturns could impact Pave's growth due to reliance on VC-backed clients.

Differentiation

Pave offers real-time compensation data, eliminating the need for spreadsheets.

Seamless integration with HRIS, ATS, and Cap Table systems sets Pave apart.

Pave's platform is powered by the largest real-time compensation dataset globally.

Upsides

Partnership with UKG enhances Pave's platform with valuable organizational data.

Pave's subscription model aligns with growing trends in enterprise software.

Increased focus on pay equity boosts demand for Pave's data-driven solutions.

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