Devops Data Engineer

As a DevOps Data Engineer at Gaming Innovation Group, you will be tasked with designing, implementing, and maintaining scalable data pipelines and ETL processes. You'll collaborate with data scientists and analysts to identify data requirements and offer effective solutions. Additionally, your role will involve automating deployments while ensuring the reliability and performance of data services in production environments. You'll also be responsible for cloud data storage management and implementing security measures as per regulations.

To be considered for the DevOps Data Engineer position at Gaming Innovation Group, you need a Bachelor's degree in Computer Science, Engineering, or a related field. Strong proficiency in programming languages such as Python, Java, or Go is a must. Applicants should have experience with data warehousing solutions, containerization technologies like Docker, and orchestration tools like Kubernetes. A solid understanding of CI/CD pipelines and cloud services is also essential.

DevOps Data Engineers at Gaming Innovation Group enjoy an array of benefits that foster both professional and personal wellbeing. These include great career development opportunities, an international health insurance plan, a health and wellbeing package worth 350 EUR per year, additional time off on your birthday, and a 'Me Time' day off annually. Plus, enjoy free lunches in the office!

At Gaming Innovation Group, the hybrid work model allows you to find the right balance that suits your personal and professional life. This means you'll have the flexibility to work from home or visit one of our offices located in Denmark, Malta, Riga, or Spain. The aim is to create a supportive and adaptable work environment, fostering collaboration and innovation within the team.

As a DevOps Data Engineer at Gaming Innovation Group, you'll work with cutting-edge technologies such as cloud platforms like AWS and Azure, along with programming languages including Python, Java, or Go. Familiarity with containerization technologies such as Docker, orchestration with Kubernetes, and strong understanding of CI/CD pipelines and data warehousing solutions is vital for the role.

When answering this question, detail your previous projects where you designed and implemented data pipelines. Discuss specific tools and technologies you used for ETL processes, such as Apache Kafka or Talend, and highlight any performance optimizations you achieved.

Share instances where you implemented monitoring tools or automated testing to ensure data service reliability. Discuss any challenges you faced and how you resolved them to maintain service performance.

Talk about your proficiency in using tools for automating deployments, like Jenkins or GitLab CI/CD. Provide examples of how your automation efforts improved workflows and reduced manual errors in previous roles.

Emphasize your communication skills and your method for gathering data requirements. Share examples of successful collaborations where you translated technical concepts into actionable data solutions.

Provide details about your experience with cloud services, particularly AWS or Azure. Discuss specific services you utilized and how you integrated them into your data engineering projects.

Explain the concept of CI/CD pipelines and how they support continuous integration and deployment. Share the tools you've used and the benefits of implementing CI/CD in your projects to enhance collaboration and efficiency.

Discuss various techniques for optimizing data flows such as partitioning, indexing, and caching. Provide examples of how these strategies led to performance improvements in your previous work.

Share your knowledge of data security best practices and any compliance standards you have adhered to, such as GDPR. Talk about specific measures you've implemented to protect sensitive data during processing and storage.

Mention the programming languages you excel in, particularly Python, Java, or Go. Describe projects where you utilized these languages effectively and how they suited data engineering requirements.

Detail your experience with tools like Docker and Kubernetes. Explain how you've leveraged these technologies to improve deployment pipelines and streamline the development process in previous data engineering roles.