Senior Data Engineer (m/f/d)

As a Senior Data Engineer (m/f/d) at Artefact, your role will encompass a variety of responsibilities, including innovating and collaborating with cross-functional teams to define client needs and solutions. You'll be responsible for building and optimizing data pipelines and maintaining the architecture of our solutions, ensuring they perform well and scale effectively. Additionally, you'll actively participate in knowledge sharing within the team and the larger community, train others, and present your work at events.

The ideal candidate for the Senior Data Engineer (m/f/d) position at Artefact will have a background in software engineering, with hands-on experience in DevOps practices. Proficiency in Python is essential, along with familiarity in using cloud technologies like GCP or AWS. Experience with big data tools such as Flink, Spark, or Beam, along with tools for data indexing like ElasticSearch, is highly valued. Additionally, a strong understanding of data pipelines, machine learning models, and the command line is required.

Artefact is committed to the growth and development of its team members. As a Senior Data Engineer (m/f/d), you'll have access to regular training sessions, coaching, and opportunities for personal advancement. The company offers various resources for professional development, including Lunch & Learn sessions and the chance to work on exciting international projects. Advancement is based on personal performance rather than tenure, ensuring you can swiftly progress in your career.

At Artefact, Senior Data Engineers (m/f/d) work with cutting-edge technologies including Google Cloud Platform (GCP), Terraform, DBT, Vertex AI, and Python. You'll be involved in setting up and optimizing data pipelines, as well as leveraging tools such as Kafka or PubSub for data bus management. Familiarity with Docker and Kubernetes (K8S) is also beneficial, allowing you to manage containerized applications effectively.

Artefact fosters a strong team spirit that encourages collaboration and innovation among its employees. As a Senior Data Engineer (m/f/d), you'll be part of a supportive environment that values knowledge sharing, open communication, and continuous improvement. With a focus on addressing real-world problems through data-driven solutions, you’ll work alongside professionals from diverse backgrounds who are passionate about leveraging AI and data to deliver measurable business results.

When answering this question, consider outlining specific projects where you successfully designed and implemented data pipelines. Highlight your role, the technologies used, any challenges faced, and how you optimized performance and scalability.

Discuss your methods for sharing knowledge, such as mentoring peers, leading training sessions, or contributing to documentation. Mention how you foster a collaborative learning environment and encourage others to engage in knowledge sharing.

Provide examples of projects where you've utilized cloud services. Discuss any specific tools or features from GCP or AWS that you found particularly useful in your work, and how they helped solve a problem or improve efficiency.

Demonstrating your ability to simplify complex concepts is crucial. You should outline a specific example where you successfully conveyed technical information to a non-technical stakeholder, ensuring clarity and engagement.

Address your project management strategies, such as prioritization techniques and tools you use to track progress. Emphasize your ability to communicate effectively with the team and manage expectations, citing any past experiences where you successfully met or exceeded deadlines.

Discuss your experience with specific big data technologies like Apache Spark, Flink, or Beam. Provide examples of how you've applied them in projects and the impact they had on data processing and analysis.

Share a detailed story of a particularly challenging project, focusing on the complexities involved, your approach to problem-solving, and the successful outcome. Highlight the skills and knowledge you utilized in overcoming obstacles.

Explain your approach to maintaining data quality, such as implementing validation processes or conducting audits. Discuss any specific security measures you take to protect sensitive data, emphasizing the importance of compliance and industry standards.

Discuss your methods for staying informed on industry developments, such as following relevant blogs, attending conferences, and participating in professional networks. Mention any specific sources or communities you engage with.

Highlight your experience in working with diverse teams, whether in data engineering or collaborative projects. Emphasize your communication skills and your ability to navigate varying perspectives and expertise to achieve a common goal.