Data Engineer

As a Data Engineer at GWI in Athens, your main responsibilities will include designing and implementing end-to-end data pipelines, collaborating with multidisciplinary teams to deliver customized data solutions, and empowering business users across various departments to extract valuable insights from data. You'll be an integral part of our strategy to enhance data-driven decision-making across the organization.

To apply for the Data Engineer position at GWI, you should have proven experience as a Data or Analytics Engineer, proficiency in SQL and Python, and expertise in designing end-to-end data solutions. Hands-on experience with cloud data stacks and tools like BigQuery, Airflow, and dbt is also essential for this role. Additionally, familiarity with software development best practices and agile methodologies is highly valued.

As a Data Engineer at GWI, you can expect a hybrid work environment that combines flexibility with the collaboration of our friendly Athens office. You’ll enjoy the benefits of working from home, alongside opportunities to engage with your team in person, making for a balanced and productive work-life integration.

To succeed as a Data Engineer at GWI, strong problem-solving skills, excellent communication, and a collaborative mindset are vital. You should also demonstrate expertise in data pipeline design, a willingness to learn new technologies, and the ability to work in a fast-paced, ever-changing environment. Being open-minded and ready to innovate will also enhance your impact within the team.

GWI provides a competitive benefits package for its Data Engineers, which includes generous annual leave, employee share schemes, private health insurance, and a commitment to work-life balance with flexible working arrangements. Furthermore, they offer professional development opportunities, including training budgets and paid international conferences, ensuring you grow within your career.

In answering this question, emphasize your practical experience with SQL and Python, highlighting specific projects where you've applied these skills. Mention any challenging scenarios you faced and how you overcame them to deliver effective data solutions, showcasing your problem-solving capabilities.

When discussing your approach to designing data pipelines, describe your methodology in detail. Highlight your understanding of data modeling, transformation processes, and orchestration tools, mentioning any relevant frameworks or technologies you have experience working with, such as dbt or Airflow.

In your response, provide specific examples of how you’ve utilized Agile methodologies, such as Scrum or Kanban, in previous projects. Discuss how this approach facilitated incremental delivery and fostered collaboration within the team, resulting in higher quality outputs and satisfied stakeholders.

Addressing data quality and governance, outline your understanding of best practices including data accessibility, cataloging, and quality assurance. Describe how you implement quality checks and balances within your data pipeline processes, and provide examples of how this has benefited past projects.

Share a project that showcases your skills as a Data Engineer, detailing your role in the project, the challenges you faced, and how you intentionally applied engineering best practices. Emphasize the project’s impact on the organization and how it contributed to the business's data-driven goals.

Discuss how you actively engage in continuous learning to keep up with new technologies and trends in the field of data engineering. Mention resources such as online courses, industry conferences, or relevant podcasts that you utilize, and demonstrate your commitment to professional development.

Illustrate your ability to bridge the gap between technical and non-technical stakeholders by sharing specific strategies you’ve used for effectively communicating complex data concepts. Discuss how you tailor your language and visuals to enhance understanding, ensuring that all parties are aligned and informed.

Reflect on the evolving landscape of data engineering, highlighting challenges such as data integration, ensuring data quality, and scalability. Share your insights on navigating these challenges and how a proactive approach to problem-solving can mitigate potential issues.

Identify the tools and technologies you have experience with in data engineering, such as cloud platforms, data transformation tools, and workflow orchestration technologies. Discuss how you have effectively implemented these tools in your projects and the outcomes achieved.

Convey your enthusiasm for the opportunity by expressing admiration for GWI's mission and values. Highlight how the role aligns with your career aspirations, and mention specific aspects of the company culture or projects that resonate with your personal and professional goals.