FAQs
What is the duration of the Data Engineer Internship?
Internships within the Data Engineer team usually last around 8 weeks.
What academic background is preferred for this internship?
A degree in a quantitative field, such as computer science, engineering, statistics, operational research, data science, or equivalent experience is preferred.
What skills are required for the Data Engineer Internship?
Candidates should have strong programming skills, deep knowledge of data importing, exporting, visualization, feature engineering, and be proficient in developing dashboards in platforms like Tableau, Qlik, or PowerBI, among other skills.
Will there be opportunities for full-time positions after the internship?
Yes, top-performing interns may be considered for entry-level Data Engineer positions upon completion of the internship.
Is there any specific application deadline?
Yes, applications must be submitted no later than 9am, Friday 4th October.
What will the application process involve?
The application process will include pre-screening questions, an online selection test to demonstrate reasoning skills, and initial interviews via Zoom, followed by a final interview day in the London office.
Does the internship involve working with clients?
Yes, interns can expect to work on client projects and contribute to analysis and results alongside experienced team members.
Are there any travel requirements for the internship?
While specific travel requirements are not stated, there is the possibility of working on cross-border projects with other Simon-Kucher offices around the world.
What are the primary responsibilities of interns in this role?
Responsibilities include helping clients analyze data, developing data models and reporting solutions, creating ETL processes, and translating business requirements into technical solutions, among other tasks.
What is the work environment like for the Data Engineer Internship?
The work environment is described as entrepreneurial and inspiring, with lots of responsibilities and a steep learning curve in a fast-paced commercially agile setting.