Senior Data Engineer

As a Senior Data Engineer at Autofleet, your key responsibilities will include designing and maintaining data pipelines for both batch and real-time processing, owning the backend data infrastructure, and collaborating with various teams to translate data requirements into scalable solutions. You'll also focus on optimizing ETL/ELT workflows and ensuring data quality while operating within cloud platforms like GCP.

To qualify as a Senior Data Engineer at Autofleet, you should have over 4 years of experience in backend data engineering or software development, proficient programming skills in Python for building production-grade data services, as well as a solid understanding of SQL. Familiarity with orchestration tools, CI/CD practices, and experience in cloud environments (AWS, GCP) is also essential.

The Senior Data Engineer role at Autofleet emphasizes proficiency in Python, SQL, and experience with data pipeline technologies like Spark. Additionally, prior experience in building data platforms and familiarity with orchestration tools such as Airflow, along with a solid understanding of cloud-native environments like GCP or AWS, is highly valued.

In the Senior Data Engineer role at Autofleet, machine learning plays a critical part as you will help power advanced analytics and optimization algorithms. Your data pipelines will support real-time machine learning applications, translating raw data into actionable insights to drive mobility solutions.

The Senior Data Engineer at Autofleet will collaborate with various cross-functional teams, including product managers and development squads. This collaborative environment encourages knowledge sharing and collective problem solving, making it essential to be an effective communicator who enjoys teamwork and collaboration.

In your response, focus on specific projects where you designed and implemented data pipelines. Mention the tools you used, such as Spark or Airflow, and how you ensured reliability and efficiency in processing. Highlight any challenges you overcame and the results achieved.

To answer this question effectively, choose a specific challenge related to data ingestion or pipeline performance. Explain the problem, the steps you took to analyze and resolve it, and the impact your solution had on the project or team.

Discuss the practices you follow to ensure data quality, such as validation checks, monitoring tools, and automated alerting systems. Provide examples of how you’ve implemented these practices in past roles to maintain high data integrity.

Share your hands-on experience with cloud services, emphasizing any specific projects where you utilized GCP or AWS for data storage, processing, or orchestration. Highlight the benefits of cloud environments and how they enhanced your data engineering projects.

Explain how you've used orchestration tools like Airflow in your previous roles to schedule workflows, manage dependencies, and monitor tasks. Provide examples of how these tools helped streamline processes and improve efficiency.

Discuss your understanding of scaling strategies, including horizontal and vertical scaling, load balancing, and distributed processing. Provide examples of where you’ve applied these methods to accommodate growing data loads or user requests.

Talk about the resources you use to keep yourself informed, such as online courses, webinars, tech blogs, and community events. Mention any recent trends in data engineering that you are particularly excited about and how they could impact your work.

Provide a specific example that showcases your ability to work with different teams, such as product managers and software developers. Highlight how you approached the collaboration, any challenges faced, and how your communication skills facilitated a successful outcome.

In your answer, explain any projects where you utilized Spark for data processing. Discuss the specific tasks handled, the volume of data processed, and any optimizations you employed to enhance Spark’s performance.

Reflect on a significant project or accomplishment in your career as a data engineer that had a meaningful impact. Consider metrics or results to demonstrate the success of your achievement and communicate how it exemplifies your skills and contributions to the field.