Data Engineer

As a Data Engineer at Zefr, your primary responsibilities include designing and operating complex workflows using Apache Airflow, developing data transformation pipelines using Snowflake and dbt, and managing distributed data science inference systems across multi-cloud environments, including AWS and GCP. You will also collaborate with team members to enhance data processes and ensure efficient implementation of technology solutions.

To apply for the Data Engineer position at Zefr, you should have a Bachelor’s or Master’s degree in an engineering discipline and ideally 1-2 years of professional experience in a technical field. You must be competent in Python and SQL, possess a good understanding of systems engineering principles, and display strong communication and collaboration skills.

In the Data Engineer role at Zefr, you will work with a variety of technologies including Apache Airflow for workflow management, Snowflake for data warehousing, dbt for data transformations, and cloud services such as AWS and Google Cloud Platform. You’ll gain hands-on experience with the latest data engineering tools, enhancing your expertise in the field.

Working as a Data Engineer at Zefr comes with a range of benefits including flexible paid time off, comprehensive medical, dental, and vision insurance, paid parental leave, and a 401(k) plan with company match. You’ll also enjoy summer Fridays, in-office lunches, and various professional development opportunities to help you grow in your career.

The compensation for a Data Engineer at Zefr ranges from $110,000 to $130,000, depending on factors such as your job-related skills, experience, and educational background. If your compensation expectations fall outside this range, Zefr encourages you to still apply as they value conversations around pay and are open to discussions.

When responding to this question, focus on your hands-on experience with Apache Airflow, detailing specific projects where you used it. Highlight how you designed workflows, optimized processes, or resolved challenges you faced while implementing Airflow solutions.

Elaborate on the data transformation techniques you have used, particularly in dbt and Snowflake. Discuss the processes you implemented, any challenges you encountered, and how you ensured data integrity and accuracy in your transformations.

When answering, discuss the methods and tools you use to maintain data quality and integrity. You can mention specific practices such as automated data validation, regular monitoring, and data cleansing techniques you have implemented in past projects.

In your response, provide a specific example of a challenging project, detailing the objectives, your role, and the technologies you used. Describe the challenges you faced, how you overcame them, and the outcomes of your efforts.

Discuss your proficiency in Python and SQL, sharing examples of projects or tasks where you utilized these languages. Explain how you've used Python for data manipulation and SQL for querying and managing data in relational databases.

In your answer, mention the cloud platforms you are experienced with, particularly AWS and GCP. Discuss specific projects where you utilized these platforms, the services you leveraged, and how they contributed to your data engineering efforts.

Describe your collaborative approach by highlighting past experiences working alongside data scientists. Discuss how you aligned data engineering processes with data science goals to ensure project success and effective data utilization.

Outline the steps you follow to design an efficient data pipeline, including understanding the data source, defining transformation requirements, selecting appropriate technologies, and implementing monitoring practices to ensure ongoing performance.

Mention the sources you follow for industry trends, such as blogs, online courses, webinars, or professional groups. Highlighting continuous learning and professional development can show your commitment to staying at the forefront of data engineering.

In your answer, discuss key data security principles, emphasizing the importance of data encryption, access controls, and compliance with regulations. Share any experience you have in securing data in your engineering practices.