Data engineer - job 1 of 2

As a Data Engineer at Writer, your main responsibilities will include building and maintaining data pipelines, ensuring data quality through orchestration, developing reliable data models, and documenting all aspects of our data processes. You'll collaborate with product and engineering teams to provide effective data solutions and help establish data engineering best practices.

To apply for the Data Engineer position at Writer, you should have at least 5 years of hands-on experience in data engineering, particularly within a SaaS environment. It's essential to be an expert in SQL, dbt, and Python, along with having knowledge of tools like Airflow and Kafka. Experience with cloud services such as AWS or GCP is also crucial to succeed in this role.

Technical skills vital for a Data Engineer at Writer include proficiency in SQL, dbt, and Python, as well as familiarity with data engineering tools and frameworks like Airflow, and data orchestration platforms. Understanding data pipelines, ETL processes, and having experience in a cloud environment will also ensure your success.

Writer places a strong emphasis on data quality and governance for Data Engineers. You will be responsible for implementing orchestration tools and designing tests to ensure data consistency and integrity. Additionally, your documentation efforts will help create a robust foundation for our data governance program, enabling the organization to trust the data they rely on.

At Writer, a Data Engineer can expect a supportive and collaborative work environment that encourages innovation. With a hybrid work model, you’ll have the flexibility to manage your time while working closely with cross-functional teams, contributing to meaningful projects that leverage data to drive decision-making.

When answering this question, detail your previous roles where you successfully built, optimized, and maintained data pipelines. Discuss the tools you used, such as SQL or dbt, and any challenges you faced during the process, along with how you overcame them.

To effectively answer, describe your approach to data validation, including any orchestration tools you've implemented. Highlight your experience with designing tests and how ensuring data accuracy has played a crucial role in your past work.

Discuss the types of data models you have developed in previous roles. Provide examples where you optimized models to suit scaling needs and how your models contributed to better decision-making in the organization.

You can showcase your organizational skills by describing your methods for assessing task urgency and importance. Mention any tools or methodologies you use, like Agile or Kanban, to manage your workload effectively.

Clearly outline your experience with cloud services, such as AWS or GCP. Provide specific examples of how you have utilized these environments to enhance data engineering tasks or improve your team's data capabilities.

Use this opportunity to demonstrate your problem-solving abilities by detailing a specific challenge you faced in data engineering. Discuss the steps you took to analyze the problem, the solution you implemented, and the positive outcome that followed.

Discuss the tools you regularly employ, such as BigQuery, Airflow, or dbt. Explain why you prefer these tools and how they enhance your efficiency and productivity in managing data workflows.

Emphasize your commitment to continuous learning by mentioning resources you follow (blogs, webinars, conferences) and specific examples of how you've applied new knowledge to your work in data engineering.

Highlight the importance of documentation in data engineering. Describe your process for maintaining clear and organized documentation of data pipelines and systems, ensuring ease of access for your team and future onboarding processes.

Outline your communication strategies, such as using visual aids or analogies, to help bridge the gap between technical and non-technical stakeholders. Illustrate this with an example where your effective communication made a significant difference.