Data Engineer

At Gratia, we believe potential is globally distributed, but opportunity is not. Our mission is to turn great talent's potential into fulfilling global careers through professional development and tech-enabled platforms. We bridge the gap between ambitious companies and hands-on experts who deliver real results instead of just theoretical plans.

Gratia is a remote-first, apprenticeship-based platform where opportunity is defined by what you can do and deliver. Our clients include global names like USA Today Co., Endeavor, and New York Life. We maintain an 88.2 Net Promoter Score because we focus on rigorous execution, not just high-level advice.

About the engagement

We are assembling a dedicated data engineering pod to embed directly within a leading private equity firm's newly acquired portfolio companies. When this firm buys a business, the legacy data environment is typically unstructured and messy. This pod's mandate is to walk in during the critical post-merger integration phase, figure out what data matters most to the investment thesis, and build the pipelines to deliver it.

We are hiring for two remote, US-based data engineers to form the foundation of this pod. Both roles require travel to the portfolio company for an initial six-week sprint before transitioning to a hybrid/remote model.

This is a remote, consulting role designed for a long-term engagement, with the potential to build an offshore team as needed.

Contract rate: up to $100/hr

This is the technical engine of the pod. You will focus entirely on execution: building the pipelines, untangling legacy systems, and writing the code required to turn disparate data into reliable commercial insights. This is a heads-down builder seat, not a client management role.

What we're looking for:

• 5+ years of hands-on experience in data engineering or data architecture, or equivalent experience.

• Deep proficiency in SQL, Python, and modern cloud data stacks.

• Track record of delivering repeatable data processes and pipelines from scratch in messy data environments.