Candidate
Employer
Entry-level Data Professional
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Job Description
Junior Data Engineer/Junior Data Scientist | Capgemini
The Tone:
This is a full-time role at Capgemini. Capgemini is a global business and technology transformation partner, comprised of 340,000 team members in over 50 countries, dedicated to accelerating the dual transition to a digital and sustainable world. This role matters because it contributes to helping leading organizations unlock the value of technology, addressing their business needs while building a more sustainable and inclusive world. It offers an opportunity for recent graduates to start their career by working with data in a supportive global environment.
The TL;DR
• Role: Early Career
• Type: Full-time
• Pay: $60000–$65000 yearly
• Mission: This person will help organizations unlock the value of technology by working with data and contributing to data engineering and analysis efforts.
• Tech Stack: SQL, Python, Tableau, Power BI
What You’ll Actually Do
• Data Management: Assist in the collection, cleaning, and transformation of data for analytics and reporting purposes.
• Query Optimization: Write and optimize SQL queries for efficient data extraction and manipulation.
• Pipeline Support: Support the development and maintenance of data pipelines and databases.
• Insight Generation: Collaborate with senior team members to analyze datasets and generate valuable insights.
• Process Improvement: Document processes and contribute to continuous improvement initiatives within the team.
The Must-Haves
• Background: Bachelor’s degree in Computer Science, Information Technology, or a related field. This is an entry-level position for a professional with a strong foundation in Computer Science.
• Experience: Academic experience through projects involving data analysis, database design, or similar data-related work.
• Skills: Strong knowledge of SQL, basic understanding of data structures, algorithms, and relational databases, along with good problem-solving skills and attention to detail.
• Bonus: Familiarity with Python or other programming languages for data manipulation, exposure to data visualization tools such as Tableau or Power BI during academic projects, and an understanding of basic concepts in data warehousing or ETL processes.
