Data Engineering | Intern | Lisbon

TransPerfect; Lisboa; Presencial; EUR 30 000 - 40 000; Tempo integral

Resumo da oferta

TransPerfect in Lisbon is looking for a Data Engineering Intern to join the Data Platform team. This role offers hands-on experience with large-scale data in a production environment, focusing on designing and building ETL pipelines using modern data engineering practices.

The ideal candidate is pursuing a degree in Computer Science or a related field, has programming skills particularly in Python and SQL, and is eager to learn and collaborate within a diverse team. This internship provides mentorship and opportunities to make a tangible impact on products used by global enterprises.

Qualificações

• Bachelor or Master's degree in Computer Science, Data Engineering, Software Engineering, or a related field.
• Programming skills — preferably Python or a similar language.
• Comfortable with SQL and structured databases.

Responsabilidades

• Design, build, and maintain ETL pipelines in a production environment.
• Develop and test high-quality code for ETL pipelines (Python and Spark).
• Collaborate with data engineers and analysts to translate business requirements into data solutions.

Conhecimentos

Python programming

SQL

Curiosity and willingness to learn

Communication in English

Data engineering and big data interest

Formação académica

Bachelor or Master's degree in Computer Science or related field

Ferramentas

MongoDB

Postgres

Databricks

Descrição da oferta de emprego

About the Role

Unbabel is looking for a curious and motivated Data Engineering Intern to join our Data Platform team. As we deliver seamless, scalable translation solutions for enterprise partners worldwide, you'll gain hands‑on experience designing and building the data platform that powers Unbabel's core products and analytics. This is a unique opportunity to work with large‑scale, real‑world language and translation data in a production environment — learning how it's ingested, processed, stored, and analyzed at scale. You'll be part of a friendly, international, and diverse engineering team committed to your growth, with supportive mentors to guide you through modern data engineering workflows and cloud data platforms.

What you’ll get out of it

• Hands‑on experience with production‑grade, large‑scale data in a real‑world environment.
• Mentorship and exposure to modern data engineering practices and cloud platforms.
• The chance to contribute ideas and make a tangible impact on products used by global enterprises.
• A collaborative, diverse team culture where your growth and success actually matter.

What you’ll work on

• Design, build, and maintain batch and streaming ETL pipelines in a production environment.
• Develop and test high‑quality code for ETL pipelines (Python and Spark).
• Work with structured and unstructured data from multiple sources (mainly MongoDB and Postgres).
• Apply data warehousing techniques to a modern cloud‑based stack (Databricks).
• Help monitor, test, and validate data to maintain high standards of data quality.
• Collaborate with data engineers, data analysts and other technical teams to translate business requirements into data solutions.
• Participate in code reviews and team discussions, picking up best practices in code quality and architecture.
• Troubleshoot and debug data systems, and document your work to improve team processes.

What we're looking for in you

• Bachelor or Masters degree in Computer Science, Data Engineering, Software Engineering, or a related field — ongoing Master’s is also welcome.
• Programming skills — preferably Python or a similar language.
• Comfortable with SQL and structured databases.
• Interest in data engineering, databases, cloud services and big data.
• Curiosity, willingness to learn, and eagerness to ask questions and seek feedback.
• Ability to communicate in English and collaborate effectively in a team.

Nice to have

• Familiarity with cloud platforms (e.g. AWS, GCP, Azure).
• Previous involvement in team projects — professional, university, personal, or open‑source.