Cloud Data Engineer

As a Cloud Data Engineer at Devoteam, you will be responsible for designing, building, and maintaining robust data models, utilizing techniques such as Kimball or Data Vault. Your primary tasks will include developing scalable data pipelines in Python, collaborating with business stakeholders on reporting needs, and implementing data visualizations and dashboards in modern tools like Looker. Additionally, ensuring data quality and documentation is critical throughout the delivery process.

To qualify for the Cloud Data Engineer role at Devoteam, candidates should possess strong SQL skills and experience with data modeling techniques, including Kimball and Data Vault. Proficiency in Python for data processing and transformations is essential, alongside hands-on experience with data visualization tools such as Looker or Power BI. A Bachelor's degree in Computer Science, Engineering, or a related field is required.

In the role of Cloud Data Engineer at Devoteam, you will work with SQL, Python, and modern data visualization tools like Looker and Tableau. Experience with BigQuery or Terraform for infrastructure as code, as well as data pipeline management tools like Dataform or dbt, is advantageous for this position.

At Devoteam, soft skills play a vital role in the success of a Cloud Data Engineer. Strong interpersonal and communication abilities are essential, along with a client-oriented mindset. Being self-driven, curious, and eager to learn will help you thrive in our innovative environment. A focus on quality, continuous improvement, and the willingness to clarify requirements will set you apart.

Devoteam provides a competitive salary package for Cloud Data Engineers, along with an extensive benefits package, including private health insurance, flexible working hours, and the opportunity to work remotely for one month per year. Additionally, employees have access to an annual bonus scheme, professional training, and certification opportunities, providing a path for personal and career development.

When answering this question, explain your methodology with emphasis on the principles of data modeling, such as normalization and denormalization. Discuss how you utilize frameworks like Kimball or Data Vault, and include your experience in collaborating with stakeholders to understand their data needs.

Prepare to detail a specific data pipeline project, focusing on the challenges you faced and your problem-solving abilities. Mention the technologies used, the architecture implemented, and how you ensured data accuracy and efficiency throughout the pipeline's operation.

List the visualization tools you have worked with, such as Looker, Power BI, or Tableau. Discuss your experience in creating dashboards and how you select the appropriate tool based on project requirements, making sure to provide examples of insights drawn from your visualizations.

Address your methods of ensuring data quality, such as implementing data validation checks, performing regular audits, and collaborating with team members for thorough documentation. Stress the importance of maintaining data integrity throughout the delivery process.

Be sure to elaborate on your Python programming skills, focusing on libraries like Pandas or NumPy that facilitate data manipulation. Highlight projects where you utilized Python for ETL processes and demonstrate your ability to transform and analyze large datasets efficiently.

Discuss your commitment to continuous learning through attending webinars, participating in online courses, and engaging with data engineering communities. Mention any specific resources you follow, like publications, blogs, or influential professionals in the industry.

If you have experience with tools like Terraform, elaborate on how you've utilized them in automating the setup of your cloud resources. Discuss the advantages you’ve realized from using infrastructure as code and its impact on operational efficiency.

Articulate the key principles of both data modeling methodologies, explaining how Kimball is typically employed for dimensional modeling while Data Vault is used for agile data warehousing. Discuss situations when one might be preferred over the other, emphasizing the decision-making process.

Emphasize your communication and interpersonal skills by explaining how you facilitate discussions with stakeholders to identify their needs. Illustrate your approach to gathering requirements effectively to ensure the data solutions provided align with business objectives.

Describe your task prioritization strategy, such as using project management tools or methodologies like Agile or Scrum. Highlight how you balance short-term deadlines with long-term project goals while maintaining open communication with your team.