Data Engineer - Allegro Pay

As a Data Engineer at Allegro Pay, your main responsibilities will include designing, monitoring, and improving data flow processes, implementing Data Mesh processes, and optimizing cloud operational costs. You'll work with Python, SQL, Airflow, and various cloud technologies to create efficient data-driven solutions, ensuring they meet the high standards we uphold.

To qualify for the Data Engineer role at Allegro Pay, you should have at least 2 years of experience building data-driven solutions with Python. A solid foundation in SQL query optimization, experience with large datasets, and the ability to work with cloud technologies like Snowflake and Azure are also essential. Familiarity with CI/CD pipelines and IaC tools is a plus.

As a Data Engineer at Allegro Pay, you'll get hands-on experience with cutting-edge technologies such as Snowflake, Airflow, and GCP, as well as modern data frameworks. We prioritize using tools that enhance our data processing capabilities, allowing you to grow within our tech stack continuously.

At Allegro Pay, you’ll have the chance to work on exciting projects that impact millions of users, with a team that values collaboration and innovation. We also provide excellent work-life balance, an array of benefits, and opportunities for professional growth through training and conferences. Plus, you’ll have access to the latest technology and tools!

Absolutely! At Allegro Pay, we support career development through an internal educational platform, training budgets, and opportunities to attend conferences. As a Data Engineer, you’ll be able to expand your skill set and take on new challenges, contributing significantly to your professional growth.

When discussing your experience with Python, be specific about projects you've completed, including the libraries you used. Highlight how you've implemented data processing applications and the impact it had on efficiency or performance.

In response, mention key techniques you've employed to optimize SQL queries, like indexing or analyzing query execution plans. Discuss any tools you’ve used for monitoring performance and how they inform your optimization strategies.

Share your step-by-step approach to building data pipelines, discussing how you've utilized tools like Airflow or dbt. Emphasize your attention to the specific needs of data processing and any challenges you've successfully overcome.

Discuss a specific challenge, outlining the problem, the steps you took to resolve it, and the results. Focus on your analytical skills and the lessons learned from the experience to highlight your problem-solving capabilities.

Speak about your hands-on experience with Snowflake or any other cloud database technology, mentioning how you've interacted with various data formats and improved performance or cost-efficiency in previous roles.

Emphasize the importance of code reviews, unit testing, and following best practices for writing clean code. Discuss any specific methodologies you adhere to that ensure high code quality in your projects.

Share your experience deploying containerized applications, mentioning technologies such as Docker or Kubernetes. Provide examples of how containerization has benefited data engineering processes in past projects.

You might want to mention specific resources you follow, such as blogs, webinars, or conferences. Highlight how continuous learning has shaped your professional development and your approach to new technologies.

Describe the steps you would take to identify and resolve data integrity issues, emphasizing the importance of data validation and testing. Make sure to mention collaboration with other teams to ensure comprehensive solutions.

Share examples of collaboration with cross-functional teams, focusing on the dynamics of teamwork in data projects. Highlight how effective communication and collaboration have contributed to your past project's success.