Senior Data Engineer

As a Senior Data Engineer at our company, you will be responsible for translating business challenges into data-driven solutions, collaborating with various teams to align business needs, and developing reliable ETL pipelines using PySpark. Your role will also encompass managing Databricks assets, leading technical vision, and mentoring teammates to ensure best practices in data engineering and cloud solutions.

To qualify for the Senior Data Engineer role at our company, you should have at least 4 years of experience in Data Engineering with a strong background in Azure DataBricks or similar cloud platforms. Proficiency in Python, SQL, and knowledge of the machine learning lifecycle are essential, along with excellent collaboration and communication skills.

Our company values continuous learning for Senior Data Engineers. You'll have access to a dedicated training budget for attending conferences, online courses, and obtaining new resources. Additionally, you'll engage in professional development workshops and coaching sessions tailored to accelerate your skills and career growth.

We prioritize work-life balance for our Senior Data Engineers by offering flexible working hours and unlimited work-from-home options. We also provide a Personal Perks Pack to support your work-life needs, alongside social events and team-building activities for deeper connection with colleagues.

The salary for a Senior Data Engineer at our company ranges from 4793 € to 5785 € per month gross, depending on your experience and competencies. We ensure that compensation aligns with the value you bring to our teams.

In your answer, highlight specific projects you've worked on using Azure DataBricks. Discuss how you've utilized its features for ETL processes, data management, and how it enhanced project outcomes. Present metrics or results if possible.

Discuss your strategy for understanding stakeholder needs and translating them into data solutions. Give examples of how effective communication and proactive engagement helped align business objectives with data engineering efforts.

Talk about various methods you employ to maintain data integrity, such as ETL testing, data validation checks, and monitoring tools. Share experiences of how you improved data accuracy in past projects.

Explain the role of ETL in data engineering landscapes and provide examples of pipelines you've built, emphasizing efficiency improvements, speed, and scalability, especially using technology like PySpark in Databricks.

Discuss your methodology for data modeling, including logical and physical design principles. Mention tools and techniques you use to optimize data structures for performance and usability.

Share your philosophy on mentorship, signifying support and guiding junior engineers through projects. Give examples of how you've helped others grow their technical skills and confidence in the workplace.

Mention specific tools you have experience with, such as GitHub Actions or others, and explain how you've integrated CI/CD practices into your workflows to ensure seamless deployment and consistency.

Articulate your conflict resolution strategies, emphasizing open communication, actively listening to different viewpoints, and mediating discussions to foster a collaborative atmosphere.

Highlight experience you have with technologies like Kafka or Azure Event Hubs. Discuss relevant projects, the data flow involved, and how streaming improved real-time analytics or decision-making processes.

Discuss the resources you utilize, such as online courses, webinars, tech meetups, or industry publications. Explain your commitment to continuous learning and adapting to new trends and tools in data engineering.