Working student Product - Data Sourcing

As a Working Student in Product - Data Sourcing at Statista, your key responsibilities will include extracting data from various internal and external sources, conducting online research to identify valuable data inputs, performing data cleaning and transformation tasks utilizing Excel and Python, building basic ETL pipelines, and assisting with mapping and other research tasks as needed. This role will provide you with hands-on experience in the data landscape, right within a global leader in the business data sector.

To be considered for the Working Student in Product - Data Sourcing position at Statista, candidates should be currently enrolled in a Computer Science, data science, or related study program in Germany. Basic knowledge of Python and SQL is essential, as well as proficiency with Microsoft Office tools, especially Excel. An independent and reliable work ethic, strong attention to detail, and solid time management skills are also key to succeeding in this role.

The Working Student in Product - Data Sourcing position at Statista is designed to offer flexibility, allowing you to work 15-20 hours per week. You will have the benefit of flexible working hours to balance your academic commitments and job responsibilities, making it a great option for students seeking practical experience while managing their studies.

At Statista, working as a Working Student in Product - Data Sourcing will help you develop valuable skills in data extraction, cleaning, and transformation, as well as building your technical knowledge in Python and SQL. You’ll gain practical experience with ETL processes and improve your research capabilities, critical thinking, and attention to detail while collaborating with talented team members from diverse backgrounds.

Statista offers a range of benefits for its Working Students in Product - Data Sourcing, including the opportunity to work from abroad for 10 days a year, hybrid work options, access to an international team with social events to foster connections, career and training opportunities, a supportive workplace culture, and mental health support through platforms like OpenUp. This creates a fulfilling environment to grow your career while enjoying a balance between work and study.

When answering this question, focus on specific projects or tasks where you have successfully extracted data from various sources. Discuss the tools you used and any challenges you faced, highlighting how you overcame them. For instance, mention using Python or SQL in your academic projects to extract meaningful data.

In response to this, you can elaborate on the methods you employ to verify the accuracy of data. Discuss techniques like cross-referencing data sources, using data cleaning processes, and performing validation checks as part of your workflow before finalizing any data outputs.

Share an example where you had to adapt to using a new tool (like Python or a data-analysis software). Explain your learning strategy, which could involve online tutorials, hands-on practice, and seeking help from peers or mentors to enhance your understanding swiftly.

Express your enthusiasm for Statista's mission to provide reliable data solutions and how the role aligns with your career goals. Mention your interest in working with diverse teams and your eagerness to contribute to a leading data analytics platform that empowers decision-making.

Outline your approach to time management, including tools like calendars or task lists, prioritizing tasks based on deadlines, and maintaining flexibility. You might mention your experience in juggling coursework with part-time responsibilities and how effective organization helped you succeed.

When responding, detail a project relevant to data sourcing or analytics, specifying the objectives, methods, tools used, and the outcomes achieved. Discuss any metrics or feedback that demonstrate the project's success and what you learned from the process.

Explain your data cleaning process, including steps like identifying missing or incorrect data, utilizing functions in Excel or Python for cleaning, and ensuring the data is structured properly for analysis. Provide an example to illustrate your methodology.

Emphasize the value you see in diverse perspectives and collaborative brainstorming. Discuss how working within a multicultural environment at Statista excites you and can enhance creativity and problem-solving among team members.

Be honest about your current level, noting any projects or coursework where you’ve applied Python and SQL. If you have room to grow, express your eagerness to improve further as part of your role at Statista.

Give a brief overview of what ETL (Extract, Transform, Load) is, detailing the stages involved and its importance in data management. You can mention any coursework or projects that have covered ETL concepts and how you look forward to applying this knowledge practically in the role.