Data Engineering Intern

Role overview

This four-month, full-time paid internship places a data engineering learner inside a remote, globally distributed team that delivers applied AI projects across a wide range of client industries. The intern supports senior engineers and specialists, gaining real production exposure rather than a purely observational experience. The role is well suited to someone early in their career who wants to build a portfolio on actual shipped work.

Responsibilities

- Contribute to building and maintaining data pipelines that keep data clean, reliable, and timely. - Help implement and refine ETL pipelines together with the data team. - Bring data from various sources into warehouses, data lakes, and lakehouses. - Take part in data management work such as cleaning, validation, and transformation. - Help frame business questions as data models and measurable metrics. - Join client-facing conversations to capture requirements and share progress on deliverables. - Explore and visualize datasets to surface quality issues and distribution shifts that might affect deployed models. - Spot data quality problems and suggest improvements to the codebase.

Requirements

- Working proficiency in English, written and spoken; CV submitted in English and all internal and client communication in English. - Foundational Python skills and familiarity with data libraries. - Foundational SQL knowledge and comfort with database engines. - Prior experience manipulating and visualizing datasets. - Strong analytical and problem-solving instincts. - Bonus exposure to AWS, (Py)Spark, Airflow, data lakes, data warehouses, or Git.

Nice to have

- Curiosity and ambition to learn across different industries using a modern tech stack. - Self-direction and positivity in a fully remote, globally distributed setting. - A team-oriented, adaptable style suited to a fast-moving startup environment. - A track record of dependable, high-quality work.

Benefits and work setup

- Fully remote and flexible working arrangement. - Every other Friday off. - Paid sick days and observance of local holidays. - Fitness subscription.