Senior Data Engineer

The Senior Data Engineer at SmartDev is responsible for collaborating with stakeholders to gather requirements and understand the app’s features. This role focuses on applying changes to data pipelines, developing and implementing data quality checks, optimizing performance, and automating manual processes. You'll work closely with teams to monitor data quality metrics and support the business as needed during production activities, ensuring that our data solutions are reliable and efficient.

To be considered for the Senior Data Engineer position at SmartDev, candidates should be proficient in Python and SQL, have experience with ETL processes, and possess a solid understanding of data quality management. Familiarity with cloud platforms such as AWS, Azure, or GCP, as well as hands-on experience with tools like Apache Spark or Kafka, is essential. Strong analytical, problem-solving, and communication skills are also critical for success in this role.

As a Senior Data Engineer at SmartDev, you can enjoy a competitive salary along with 20 annual leave days, in addition to Vietnamese public holidays. Our employees also benefit from a year-end performance bonus and premium healthcare insurance. We offer a flexible working hours policy and a hybrid working environment, ensuring that you can maintain a healthy work-life balance while contributing to exciting projects.

SmartDev is committed to fostering growth and development for our team members, especially for roles like Senior Data Engineer. We provide opportunities for training and knowledge sharing and promote a collaborative environment where you can learn from talented professionals. Joining SmartDev means being part of a community that encourages innovation and continuous learning, enhancing your skill set and career progression.

As a Senior Data Engineer at SmartDev, you will work with a variety of tools and technologies that are vital for managing data pipelines and ensuring quality data. Proficiency in Python and SQL is crucial, and you will also use tools like Apache Spark and Kafka. Familiarity with cloud platforms like AWS, Azure, or GCP will enhance your work and allow you to experience the latest advancements in data engineering.

When answering this question, highlight specific ETL projects you've worked on. Discuss the tools you used, the challenges you faced, and how you overcame them. Be sure to emphasize your understanding of the importance of data quality and integrity throughout the ETL process.

To effectively answer this question, discuss your approach to implementing data quality checks and tests. Talk about how you monitor data quality metrics and the techniques you use to identify and resolve data discrepancies, emphasizing your analytical problem-solving skills.

Focus on specific strategies you have implemented to enhance performance in data pipelines, such as improving data processing times or reducing bottlenecks. Mention any tool or technology you leveraged for optimization and the impact it had on overall data processing.

This is a great opportunity to provide insight into the cloud platforms you've worked with, such as AWS, Azure, or GCP. Share specific projects where you utilized these platforms, discussing how they benefited your data engineering tasks and your ability to manage data in the cloud.

Effective communication with stakeholders is crucial in this role. Discuss how you gather requirements, manage expectations, and keep stakeholders informed throughout the data engineering process. Examples of collaborative projects can illustrate your approach well.

Share your knowledge of data modeling techniques and principles, and provide examples of how you've applied them in prior roles. Discuss the importance of data modeling in data engineering and how it impacts the overall data management process.

Express your passion for the field by discussing the resources you use to stay informed, such as blogs, webinars, or courses. Highlight how continuous learning has helped you apply new techniques in your work, especially as a Senior Data Engineer.

Use the STAR method (Situation, Task, Action, Result) to frame your answer. Focus on a specific challenge related to data quality or pipelines, explaining the steps you took to resolve it and highlighting the successful outcome.

Discuss your strategies for prioritizing tasks, such as assessing project impact and deadlines. Mention any tools or methodologies you use to manage multiple projects effectively, ensuring that you meet stakeholder expectations and project goals.

To answer this question, share your insights on upcoming trends in data engineering like real-time analytics, automation, and the growing role of AI. Discuss how you envision adapting to these changes as a Senior Data Engineer at SmartDev.