Data Engineer Intern

Data Engineer Intern

Join our data team as a Data Engineer Intern and get hands-on experience building the data pipelines that power business intelligence and analytics across the company. You will work alongside experienced data engineers who will guide you through real projects on a modern cloud data stack.

Key responsibilities

• Support the development and maintenance of data pipelines under the guidance of senior data engineers.
• Write and optimize SQL queries to extract, transform, and load data from various sources into central repositories (e.g., data lakes, data warehouses).
• Develop Python scripts for data processing, data quality checks, and automation of routine tasks.
• Assist in monitoring data pipelines, investigating data issues, and documenting root causes and fixes.
• Contribute to data documentation: data dictionaries, pipeline descriptions, and runbooks.
• Use GitLab for version control, following the team's branching and code review practices.
• Participate in code reviews as a learner, applying feedback to improve code quality.
• Collaborate with data scientists, analysts, and engineers to understand data needs and deliver small, well-scoped improvements.
• Take part in agile ceremonies (daily stand-ups, sprint planning, retrospectives) and present your work to the team.

Requirements

• Currently enrolled in (or recently graduated from) a Bachelor's or Master's degree in a STEM field (Computer Science, Engineering, Mathematics, Statistics, or similar).
• Solid understanding of SQL fundamentals (joins, aggregations, filtering) from coursework or personal projects.
• Basic programming skills in Python (data structures, functions, working with libraries such as pandas).
• Understanding of core data concepts: relational databases, data types, and the ETL/ELT process.
• Familiarity with Git (commit, branch, merge) and the command line (Linux/Bash basics).
• Eagerness to learn, attention to detail, and the ability to ask good questions.
• Good communication skills in English.

Nice to have

• Exposure to a cloud platform (AWS, Azure, or GCP) through coursework, certifications, or personal projects.
• Awareness of distributed data processing tools such as Apache Spark or Apache Kafka.
• Familiarity with workflow orchestration (e.g., Apache Airflow) or transformation tools (e.g., dbt).
• Basic knowledge of Docker.
• Academic or personal projects involving data pipelines, data analysis, or databases (GitHub portfolio welcome).

What you will learn

• How production-grade data pipelines are designed, built, orchestrated, and monitored (Airflow, Spark, Kafka, dbt).
• Working with cloud data infrastructure on AWS.
• Software engineering practices for data: version control, CI/CD with GitLab, testing, containerization with Docker and Kubernetes.
• Data governance, security, and quality fundamentals.
• How to translate business requirements into technical data solutions.

Key Attributes

• Curiosity and a problem-solving mindset when working with data.
• Willingness to learn new tools and take ownership of assigned tasks.
• Team player who communicates openly and welcomes feedback.
• Structured, organized approach to work and documentation.