Compa is a venture-backed SaaS startup revolutionizing the future of compensation.
In a dynamic job market with hiring challenges, accountability, and the rise of AI, companies need the best data to stay ahead of industry changes, competition, and costs. Compa has developed the premier real-time compensation data platform, delivering top-tier compensation intelligence to leading enterprise teams.
Compa is a compensation intelligence company built to augment enterprise compensation teams in the era of AI.
Our customers include the world’s biggest companies: NVIDIA, Stripe, DoorDash, Open AI, TMobile, Moderna, Workday, Ulta, Target, and more.
Locations:
Compa headquarters are located in Irvine, California, with growing sites in Denver, Colorado and San Francisco, California. We’re a collaborative, curious, and driven team that values transparency, ownership, and continuous learning and prioritizing in person work where possible.
The Role:
The Compensation Data Analyst plays a critical role in maintaining trust in Compa’s compensation insights by monitoring data yields, identifying systemic data issues, resolving customer-specific data problems, and supporting ongoing data releases and quality management.
Key ResponsibilitiesMonitor the health and recency of compensation data across customer accounts.
Investigate and resolve data inconsistencies to ensure reliable insights in the platform.
Partner with Product, Engineering, and Integrations teams to support stable data pipelines.
Respond to customer- and internally-reported data questions with clear, timely communication.
Execute routine data updates and validate accuracy as part of ongoing data releases.
Perform recurring data quality checks to proactively maintain data integrity.
You’ll work at the intersection of compensation expertise, data operations, and product quality.
The role offers clear ownership and visibility, with meaningful impact on customer trust and product reliability.
Minimum Qualifications:
Experience working with structured datasets and performing data validation or quality checks.
At least 3 years of experience working with compensation data and deep knowledge of compensation benchmarking
Familiarity with HR technology systems and comfort working with technology systems.
Strong analytical and problem-solving skills with attention to detail.
Ability to communicate clearly about data findings and issues with both technical and non-technical stakeholders.
Comfort working in a fast-paced, collaborative environment with evolving priorities.
Preferred Qualifications:
Exposure to data integrations or APIs.
Data analysis and proficiency in advanced data analysis (SQL, Python, etc.)
Prior experience in a customer-impacting or support-adjacent role, where clear communication and timely resolution were important.
Top Skills
What We Do
Compa is a venture-backed SaaS startup revolutionizing the future of compensation.
In a dynamic job market with hiring challenges, accountability, and the rise of AI, companies need the best data to stay ahead of industry changes, competition, and costs. Compa has developed the premier real-time compensation data platform, delivering top-tier compensation intelligence to leading enterprise teams.
Compa is a compensation intelligence company built to augment enterprise compensation teams in the era of AI.
Our customers include the world’s biggest companies: Apple, NVIDIA, Tesla, Mastercard, T-Mobile, Sanofi, Moderna, Gilead Sciences, and more.
Why Work With Us
Compa is reimagining how companies win with pay—building tools that make compensation more fair, transparent, and data-driven. You’ll join a mission-driven, remote-first team that values ownership, low ego, and real impact. If you want to shape the future of work, this is the place.
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Compa Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Compa headquarters are located in Irvine, California, with growing sites in Denver, Colorado and San Francisco, California. We’re a collaborative, curious, and driven team that values transparency, ownership, and continuous learning.