Data Engineer

GitGuardian is a global cybersecurity scale-up. The company is based in Paris, New York City, Boston.

Our early investors include Scott Chacon, co-founder of GitHub, and Solomon Hykes, co-founder of Docker, alongside leading US and European VC firms. We raised our Series C in early 2026.

GitGuardian leads the way in securing the credential layer, helping organizations protect the secrets that let code, machines, and AI agents access systems with broad detection, identity context, and remediation at scale. Already trusted by 600K+ developers worldwide and by 50 of the Fortune 500!

You will join the Data & AI Engineering team, whose mission is to build the data foundation and the internal AI-engineering capability that let GitGuardian run as an AI-native company.

The team works across three pillars. On the data side, we run the data platform, the business data models, and the ingestion chain behind every team's reporting and our product analytics, with the aim of data that is used, trusted, and increasingly near real-time. On internal AI tooling, we build the shared skills, internal MCPs, and the Analytics Agent, along with the governance and evaluation standards that make these tools safe to build on. And on apps, we maintain the internal app framework that lets anyone in the company ship an application to software engineering standards, with SSO, CI/CD, and managed hosting built in.

Data is already the backbone of the company's reporting and decision process. AI tooling and internal apps are newer ground, with real freedom to shape how all business stakeholders use data on the day-to-day.

Key challenges :

Technical environment

If you think you match at least 70% of these criteria, please apply!

Here's what we consider essential for success in this role:

The interview process

At GitGuardian, we are committed to building a diverse, equitable and inclusive workforce.

We will ask for your gender identity on the application page to help us understand the diversity of our applicant pool and to track our progress in attracting and hiring a diverse workforce. The information is optional and will not be disclosed to the hiring manager or the interview team and will not be considered in the hiring process. We appreciate your willingness to share this with us so that we can continue to improve our diversity, equity and inclusion efforts.

1. Manager interview

To discover your professional project and evaluate if there could be a mutual match

2. Technical interview with the team (1h30)

Purpose: We validate your hard skills and give you the opportunity to discuss with other staff engineers and data engineers

Skills Assessed: Python coding proficiency, Data architecture skills and overall communication and reasoning.

3. Engineering leadership interview

Purpose: To know more about yourself and your achievements, and present to you the team.

Skills Assessed: We assess your soft skills (ownership, communication) and motivation.

4.1 Final interview with Eric our CEO, onsite

Purpose: To detail our company’s vision and ambitions for the next couple of years.

You will also meet some team members for a coffee.

4.2 References check

You can start thinking about two contacts who can attest to your previous or current professional experiences. These contacts should be as recent as possible, and we will call them at the end of the process.