Senior Data Engineer

As a Senior Data Engineer at PandaDoc, you will be responsible for designing, developing, and maintaining data pipelines within the Snowflake ecosystem. You will create and optimize dimensional data models following Kimball methodology, implement data quality checks, collaborate with analytics teams to fulfill business requirements, and mentor junior team members. Additionally, you'll perform ETL/ELT development and optimize the performance of our data warehouse.

Candidates applying for the Senior Data Engineer role at PandaDoc should have a minimum of 5 years of experience in data engineering and at least 3 years of hands-on experience with Snowflake. A strong expertise in dimensional data modeling, proficiency in SQL, and knowledge of data warehouse concepts are key qualifications. Familiarity with tools like dbt, cloud platforms, and business intelligence tools is also preferred.

For the Senior Data Engineer role at PandaDoc, strong programming skills are crucial. Proficiency in SQL is essential, and experience with Python is preferred. In addition to these, a good understanding of modern data pipeline tools and transformation frameworks is highly beneficial in this position.

Teamwork is at the heart of the Senior Data Engineer role at PandaDoc. You will collaborate closely with analytics teams to align technical solutions with business needs. Furthermore, you will mentor junior data engineers, promoting best practices and encouraging a culture of shared knowledge within the team. Our company values team connectivity, even in a remote environment.

PandaDoc fosters a supportive and inclusive company culture for Senior Data Engineers, valuing work-life balance and promoting initiatives that enhance team bonding. With employees distributed globally, we use technology to maintain strong communication and a sense of camaraderie. We believe that happy employees are productive employees, which is reflected in our integrity and transparency values.

Certainly! The Kimball methodology is a design approach for data warehousing that focuses on creating dimensional data models. It emphasizes simplicity and ease of use for end-users. In an interview, discuss how you can create star schemas and snowflake schemas to facilitate data analysis, showcasing any real-world examples you've implemented.

When discussing ETL development in a cloud environment, highlight your experience with various ETL tools and your understanding of cloud technologies like AWS, Azure, or GCP. Explain how you design ETL pipelines for scalability and performance, ensuring that you monitor data quality and integrity throughout the process.

Optimizing data warehouse performance can be approached through indexing, partitioning, and efficient query design. In your response, you may want to share specific techniques you’ve implemented in Snowflake or other data warehouse platforms. Discussing performance metrics you use to track improvements can also be effective.

To maintain data quality, I emphasize the importance of adding automated quality checks within the data pipeline. Be prepared to discuss specific tools or frameworks you use for data validation and the processes you follow for monitoring data issues. An example of resolving a data quality problem could strengthen your response.

Certainly! dbt is a powerful tool for transforming data in the warehouse. When explaining your experience, focus on how dbt allows for modular SQL development and version control, enhancing collaboration among data teams. Highlight any successful projects where you've leveraged dbt to streamline the transformation process.

Documentation is crucial in data engineering, as it ensures that data architectures and processes are clearly communicated to the team. In your answer, stress the importance of maintaining comprehensive documentation for ease of knowledge transfer, troubleshooting, and onboarding new team members.

Mentoring junior data engineers involves sharing knowledge and best practices. Discuss specific methods you employ, such as regular check-ins, code reviews, and hands-on workshops. Highlight any successes you've had in helping junior engineers progress in their skills and careers.

When discussing a challenging data problem, focus on your problem-solving approach. Outline the complexities involved, the steps you took to diagnose the issue, and the ultimate solution you implemented. Sharing quantifiable results from your efforts can further demonstrate your capability.

In terms of data governance and security, it's essential to understand the frameworks that protect data integrity and compliance. Discuss your experience implementing security measures like access controls, data encryption, and your approach to ensuring compliance with relevant regulations.

Staying current with data technologies and trends is crucial in this field. Discuss the blogs, conferences, and online resources you engage with, highlighting your enthusiasm for continuous learning. Perhaps mention any courses or certifications you've recently completed as well.