Products Analytics Engineer - Internship

Overview

The Product Analytics Engineer internship is offered by Ledger within the Corporate Functions department. Based in Paris, France, the position operates on a remote work mode within the Europe region. The intern joins the Product Analytics team, which supports Ledger Wallet across product, business, and growth teams, and reports directly to the Product Data Analytics Manager.

Who can apply

Applicants must be final-year students in data science, computer science, engineering, or a related technical field who are seeking an end-of-studies internship. Candidates are required to possess strong proficiency in Python scripting, SQL, and data modeling concepts. They should also understand statistical analysis, A/B testing methodologies, and funnel evaluation, while demonstrating an analytics engineering mindset centered on reliability, data quality, documentation, and reproducibility.

What you get

Selected interns receive comprehensive compensation and benefits packages. These include health and life insurance, commuter allowances for transportation, and flexible work options featuring a hybrid policy permitting remote work up to three times per week. Additional offerings include learning and development training solutions, financial benefits such as employee shareholding opportunities, and support for workplace accommodations.

Requirements

The role requires hands-on involvement in cross-functional collaboration with data scientists, data engineers, and product managers to deliver insights and support personalization pipelines. Responsibilities include investigating and triaging tracking issues to maintain data sanity, specifying and supporting A/B test setups, and establishing AI governance and automation through custom skills and guardrails for analytics tools like Mixpanel and Snowflake MCP. Interns also develop data models and comprehensive documentation using dbt and data marts, while maintaining privacy-compliant constraints across all analytics and modeling workflows. Good written and verbal communication skills in English are necessary.