Data & Cloud Engineer (H/F)

As a Data & Cloud Engineer at fifty-five, your responsibilities encompass developing and implementing technical solutions for creating data pipelines and platforms tailored to client needs. You'll engage in retrieving and processing data from multiple sources, automate tasks, and monitor the entire setup. Your role will also involve prototyping and delivering robust production code while exploring innovative solutions in a collaborative environment.

Candidates for the Data & Cloud Engineer role at fifty-five should possess a foundational experience in data engineering, alongside a robust mastery of Python and SQL. Familiarity with cloud environments like GCP, Azure, or AWS is essential, and knowledge of tools such as Docker, Kubernetes, Apache Airflow, and Terraform will be a considerable advantage. Proficiency in data warehouse systems like BigQuery or Snowflake is also preferred.

A Data & Cloud Engineer at fifty-five will utilize a variety of cutting-edge technologies, such as Docker for containerization, Kubernetes for orchestration, and Terraform for Infrastructure as Code. They will also work with different cloud platforms like Google Cloud Platform (GCP), Azure, and AWS, alongside tools for developing and monitoring data workflows such as Apache Airflow.

Collaboration is key for a Data & Cloud Engineer at fifty-five. You’ll collaborate closely with cross-functional teams, including consultants, data analysts, and data scientists, in your daily work. Your role involves code reviews, unit testing, and agile sprints, fostering a spirit of teamwork and knowledge sharing to drive project success.

At fifty-five, employee well-being is a top priority. The company offers a beautiful office space in central Paris complete with a terrace and garden, promoting a balanced work environment. Additionally, our inclusive culture, diverse teams, and regular social events help foster a positive environment where employees feel valued and supported.

In your answer, emphasize any specific cloud providers you have worked with, detailing projects that showcase your hands-on experience with GCP, Azure, or AWS. Discuss how you’ve utilized their services to build or manage data platforms and the challenges you navigated.

When answering this question, outline your workflow from understanding client requirements to designing, building, and monitoring data pipelines. Highlight your use of tools and methodologies, such as data cleaning and automation, to ensure high-quality data processing.

Discuss your proficiency in Python and SQL, and provide examples of projects where you have used these languages. Elaborate on specific applications, such as data manipulation, querying databases, or developing automated scripts for data processing.

In your response, explain techniques you employ for data validation and cleaning, like implementing automated checks, unit testing, and using tools such as Apache Airflow. Discuss the importance of maintaining high data quality and how it impacts overall project success.

Discuss a systematic approach to troubleshooting, including logging, monitoring, and diagnosing issues with cloud deployments. Share specific challenges you've faced, how you resolved them, and tools you employed for effective troubleshooting.

Outline your understanding of Infrastructure as Code (IaC) principles and your experience with tools like Terraform. Provide examples of projects where you have implemented IaC, detailing the benefits in terms of scalability and manageability.

In your answer, focus on communication and collaboration. Explain how you’ve worked closely with data scientists on technical requirements, focusing on delivering data frameworks and tools that meet their analytical needs.

Share insights about your experience with Docker and Kubernetes, including how they improve deployment efficiency. Discuss scenarios where you've utilized containerization for applications and the advantages it brings for scalability and resource management.

Narrate your experience with a particularly challenging project, detailing the goals, your role, the obstacles faced, and the eventual outcomes. Highlight specific technical skills and problem-solving strategies you employed throughout the project.

Discuss your proactive approach to continuous learning, mentioning resources such as industry conferences, online courses, and tech blogs. Emphasize your commitment to applying new knowledge to improve your work and contribute positively to your team's objectives.