AI Data Engineer (f/m/div.)

As an AI Data Engineer (f/m/div.) at Bosch, you will design and optimize scalable data pipelines that support AI and ML models while automating the data infrastructure. You will also implement data transformation and streaming services, improve labeling workflows for efficiency and quality, and collaborate with data scientists to provide them with the necessary data solutions.

To apply for the AI Data Engineer (f/m/div.) role at Bosch, you should hold an MSc or Ph.D. in Computer Science, Data Science, or a related field. Additionally, having a minimum of one year of experience in the field, along with excellent programming skills in Python or C++, is crucial. Knowledge of data engineering practices, including data modeling and ETL development, is also important.

An AI Data Engineer (f/m/div.) at Bosch should be proficient in programming languages such as Python and/or C++. Familiarity with data engineering practices, data modeling, ETL processes, and data warehousing are key areas of expertise necessary for success in this role.

Bosch is committed to the professional growth of its employees, including AI Data Engineers (f/m/div.). They offer various training opportunities, including technical training, foreign language courses, and certifications to enhance your skills and knowledge in the field.

The work culture for an AI Data Engineer (f/m/div.) at Bosch emphasizes collaboration, inclusivity, and respect for individual authenticity. Employees are encouraged to grow together, share ideas, and support one another in achieving common goals, all while enjoying a diverse workplace.

When answering this question, focus on the specific project, detailing the challenges you faced, the tools and technologies you used (like Python or C++), and how successfully you optimized the data pipeline. Highlight your collaboration with other team members or data scientists to show teamwork.

In your response, discuss any specific frameworks or tools you've utilized for automation, such as Apache Airflow or similar technologies. Highlight how your strategies improved efficiency and effectiveness in data processing and management.

Explain the processes you have put in place for data validation, cleansing, and monitoring within your data pipelines. Mention any specific tools or methodologies that you utilize to maintain high standards of data quality.

Share examples of how you have worked closely with data scientists to understand their data requirements. Discuss the importance of communication in translating those needs into effective data solutions. It’s also helpful to describe any specific projects.

Discuss the importance of data modeling in your past projects, detailing the types of data models (conceptual, logical, physical) you've used and how that influenced the structure of your data pipeline.

Highlight best practices such as keeping ETL processes simple, ensuring efficient data extraction methods, and using version control for the ETL scripts. You may also want to mention the importance of scheduling and monitoring ETL jobs.

Share resources you rely on, such as blogs, webinars, online courses, or forums in the data engineering community. Discuss how this ongoing education helps you apply new technologies in your work.

Mention any specific cloud platforms (such as AWS, Azure, or Google Cloud) you have used and explain how you leveraged those services for data storage, processing, or analysis in your projects.

Describe your systematic approach to identifying the root cause of failures, utilizing logging and monitoring tools, and how you implement solutions while keeping data integrity intact.

Emphasize the importance of thorough documentation in facilitating clear communication and knowledge transfer among team members, enabling easier troubleshooting and onboarding of new staff.