GCP Data Engineer (3-Month Contract)

As a GCP Data Engineer with STACK IT Recruitment, you'll have the exciting task of supporting an in-house product build for a leading retail client. This involves developing and optimizing data pipelines using PySpark, implementing workflow orchestration with Airflow, and working with tools such as Pub/Sub, BigQuery, and DataProc to enhance data processing systems.

To succeed as a GCP Data Engineer at STACK IT Recruitment, you should have at least 5 years of experience in data engineering and cloud analytics, with strong hands-on experience in GCP services and the ability to develop and troubleshoot PySpark ETL pipelines. Experience with Airflow, BigQuery, and managing Cloud Storage is also essential.

The GCP Data Engineer role at STACK IT Recruitment offers a hybrid work model, where you will be required to work in the office 3-4 days per week. This allows you to collaborate closely with your team while also enjoying the flexibility of remote work.

As a GCP Data Engineer at STACK IT Recruitment, you'll work on impactful projects that involve optimizing data processing for a high-profile retail client. You'll leverage cutting-edge GCP technologies to develop and deploy solutions that significantly improve data workflows and analytics.

The hourly rate for the GCP Data Engineer position at STACK IT Recruitment ranges from $70.00 to $80.00, depending on your experience and expertise. This competitive rate is based on your skills and the high-impact nature of the projects you'll be involved in.

When answering this question, highlight your specific projects and the services you've used, such as BigQuery, Cloud Storage, or DataProc. Emphasize your role in developing data pipelines and how you've leveraged GCP to solve real-world data challenges.

Focus on your knowledge of PySpark and Airflow when answering. Discuss methods like query optimization, efficient data partitioning, and the use of real-time streaming with Pub/Sub, showcasing any improvements you've made in previous roles.

Share your approach to problem-solving in data processing scenarios, including debugging techniques, tools you use for monitoring data workflows, and any proactive measures you've implemented to prevent issues from arising.

Discuss your communication skills and methods you use to engage stakeholders effectively. Emphasize your ability to translate technical requirements into actionable tasks and how you ensure alignment with client expectations.

Use this opportunity to narrate a specific data project, detailing your role, the technologies used, and the impact of the project. Discuss challenges you encountered and how you overcame them, highlighting your problem-solving abilities.

Highlight your understanding of data governance practices, as well as techniques for securing data on platforms like GCP. Discuss compliance with regulations like GDPR and any specific measures you implement to protect sensitive information.

Discuss your experience with tools such as Pub/Sub and how you've utilized these technologies to build or improve real-time data processing systems. Provide examples of scenarios where real-time processing added value to your projects.

Talk about your proficiency in Python, emphasizing any projects where you've used PySpark to create ETL pipelines. Discuss your coding best practices, debugging processes, and how you integrate programming into your data engineering workflows.

Share your knowledge about Airflow, including how you've used it to schedule and monitor workflows. Provide examples of how you've implemented Airflow in past projects to streamline processes and improve efficiency.

Discuss your passion for data and analytics, and how you enjoy the challenges that come with the role. Mention your eagerness to work with cutting-edge technology and collaborate with others to deliver impactful solutions.