Data Analyst

To thrive as a Data Analyst in our company, you'll need a solid foundation in data analysis, particularly with security logs. Proficiency in Azure pipelines and security logging is essential, alongside expertise in data wrangling techniques like KQL and Regex. Familiarity with SIEM concepts is also beneficial. Additional skills in Linux/Unix command line and Azure networking would be advantageous for your role.

The work environment for our Data Analyst role is quite flexible. While you will be required to work on-site in Amstelveen two days a week, the remaining days offer the convenience of working from home. This hybrid model supports a healthy work-life balance while ensuring you remain connected with your team.

As a Data Analyst, you will be tasked with analyzing security logs and managing Azure pipelines. Your role will include data wrangling, incident and problem management, and contributing to service improvement proposals. You will also help in coaching new analysts and participate in hiring drives to build a robust team.

Yes, prior experience with security logging is highly desirable for the Data Analyst position. Familiarity with security information and event management (SIEM) will help you in processing and analyzing security logs efficiently, enabling you to contribute effectively to our data operations.

While there are no strict educational requirements, a background in Data Science, Computer Science, Information Technology, or a related field is preferred for the Data Analyst role. Practical experience and a strong understanding of relevant tools and programming languages will also be highly valued.

In this Data Analyst role, you will be utilizing a variety of data analysis tools, including Azure for pipeline management and KQL for querying data. A solid grasp of Regex will be essential for parsing through large data sets effectively. Additionally, familiarity with SIEM tools will enhance your ability to manage security events.

Our company is dedicated to nurturing talent and offers various opportunities for professional development for Data Analysts. This includes on-the-job training, knowledge sharing sessions, and the chance to mentor new analysts. Our commitment to continuous learning ensures that you will always have avenues to enhance your skills.

Data wrangling is essential because it allows Analysts to clean and organize raw data, making it ready for analysis. Discuss specific techniques you’ve used, like KQL and Regex, and how they benefited your past projects.

Focus on a specific project, detailing the challenges faced and your analytical process. Highlight your decision-making skills, the tools employed, and the eventual outcomes to showcase your capability in handling complex projects.

Highlight your methods for quality checks, such as cross-referencing data, using statistical tools, and validating against known benchmarks. Discuss your commitment to adhering to quality standards and regulatory requirements.

Discuss your familiarity with Azure pipelines, explaining how you’ve used them to automate data processing tasks and integrate data from multiple sources. Be prepared to provide examples of specific benefits gained.

Emphasize the importance of simplifying complex data insights into understandable terms. Describe how you use visual aids, clear language, and storytelling techniques to effectively communicate your findings.

Share a concrete example of an incident where SIEM tools were utilized. Detail your analytical approach, the steps taken to resolve the incident, and any lessons learned that enhanced your future performance.

Talk about your commitment to continuous learning through online courses, webinars, and industry conferences. Share specific resources you rely on, demonstrating your proactive approach to staying current.

Share a specific example of teamwork, emphasizing your role in the collaboration, the challenges faced, and the successful outcome achieved. Highlight how your contributions positively impacted the project.

Outline your experience with Regex, providing specific examples of how you have utilized it for data extraction or manipulation in past projects. Discuss how it has improved your efficiency in data tasks.

Discuss trends such as machine learning integration, data privacy regulations, and the rise of self-service analytics. This shows your strategic thinking and awareness of the broader data analysis landscape.