Data Engineer

As a Data Engineer at Wheely, your primary responsibilities will include enhancing the Data team's architecture, improving the codebase with dbt for data transformations and LookML for the semantic layer, and evolving our data integration pipelines with tools like Airbyte and Kafka. You will also handle feature requests, bug fixes, and data quality assurance while enforcing code quality and automated testing.

To apply for the Data Engineer position at Wheely, you should have at least three years of experience in roles such as Data Engineer, Data Infrastructure Engineer, or Analytics Engineer. A strong background in managing data pipelines, working with analytical databases like Snowflake or Redshift, and proficiency in SQL and Python are essential. Additionally, a technical degree and teamwork skills are important for success in this position.

At Wheely, Data Engineers work with a variety of cutting-edge technologies. Key tools include Airbyte and Debezium for data pipeline management, Snowflake for analytical databases, and Looker and Metabase for BI tools. Your role will also involve using Docker for application containerization and ensuring strong data quality and performance through structured data modeling techniques.

Wheely is committed to the growth of its employees, including Data Engineers. We offer professional development subsidies to help you further your skills and knowledge in the data field. This, combined with our collaborative culture and flexible working environment, ensures that you have the support and resources needed to thrive in your career.

When you join Wheely as a Data Engineer, you can expect a competitive salary and valuable employee stock options. Additionally, we provide a relocation allowance, lunch benefits, and comprehensive medical insurance (including dental). You’ll also enjoy flexible working hours and the option to work from home if needed, all while using the best-in-class equipment.

When answering how to optimize data pipelines, showcase your ability to analyze performance bottlenecks, highlight techniques you've used to improve efficiency, and discuss specific tools like Airflow or Airbyte. Cite examples from your past roles to demonstrate your hands-on experience in this area.

Discuss your familiarity and hands-on experience with cloud data warehousing solutions like Snowflake, BigQuery, or Redshift. Be ready to highlight specific projects where you've configured and optimized these databases to store and analyze large datasets efficiently.

Explain your approach to maintaining data quality, including setting up validation checks, automated tests, and monitoring tools. Provide examples of challenges you've faced in past projects and how you ensured data integrity through various techniques.

Share specific experiences of working with Docker, emphasizing how you've used it for containerizing applications. Explain why containerization is vital in your workflows and how it contributes to maintaining consistency across different environments.

Collaboration is crucial in data engineering, as you often work with cross-functional teams. Highlight your experience in collaborating with data scientists, analysts, and software engineers, and give examples of how teamwork has improved project outcomes at previous roles.

Discuss your experience with version control through Git, your understanding of GitOps principles, and any continuous integration and delivery practices you've implemented. Emphasize how these methodologies improve the efficiency and reliability of your development process.

Provide an overview of best practices for data modeling, including a DRY (Don't Repeat Yourself) approach and performance tuning techniques. Use examples from past projects where you've successfully implemented these strategies.

When discussing troubleshooting, outline your systematic methodology for identifying problems, such as monitoring logs and metrics. Share a specific example of a complex issue you resolved in a data pipeline, detailing the steps you took and the outcome.

Show your passion for the mission of Wheely and your belief in the importance of user privacy in technology. Discuss how the values and culture of Wheely align with your professional goals and the unique opportunities you see in this role.

Reflect on a significant achievement in your data engineering career, whether it’s optimizing a challenging data process, successfully implementing a new technology, or improving team collaboration. Make sure to quantify the impact of your accomplishment to showcase your effectiveness.