Senior Data Engineer

As a Senior Data Engineer at Navan, you will take on various responsibilities including designing and developing our data warehouse models, ensuring high data quality and reliability through rigorous tests and monitoring, as well as collaborating with teams across the organization to create a ‘single source of truth’ for data. Your expertise will also help establish best practices in data modeling, enabling better data self-service for users.

To apply for the Senior Data Engineer position at Navan, you should have at least 8 years of experience in data engineering or a related field, with a minimum of 5 years focused on high-scale ELT in cloud data warehouses, particularly with Snowflake. Proficiency in dbt core and AWS is essential, alongside strong skills in SQL and Python, and experience with data orchestration using Airflow.

At Navan, a Senior Data Engineer will work with an array of modern technologies including AWS, Fivetran, Airflow, DBT, Snowflake, Atlan, and Monte Carlo as part of their daily tasks to enhance and maintain a robust data warehouse that supports the company’s data needs.

Navan prioritizes data quality by implementing effective testing and monitoring strategies within the data warehouse. As a Senior Data Engineer, you will be responsible for creating definitions and metrics that support a 'single source of truth,' enhancing decision-making across the organization.

The pay range for the Senior Data Engineer position at Navan is between $138,750 and $259,000 USD. This range reflects the anticipated low and high end of compensation and is subject to change based on various factors such as skills, experience, and market demands.

When answering this question, share specific examples of ELT processes you have designed or managed, focusing on the technologies used, challenges faced, and how your contributions improved data flow and usability.

Discuss your methodology for identifying data quality issues, including any tools or techniques you utilize to enforce data integrity and reliability. Mention examples where you implemented solutions to enhance data quality.

Share your experience with automation tools such as CI/CD pipelines in managing data workflows. Emphasize specific instances where automation improved efficiency and reduced manual handling.

Explain your communication strategies and collaboration methods you use with cross-functional teams. Highlight any tools or processes that facilitate teamwork and understanding of data requirements.

Detail your hands-on experience with Snowflake, including specific features you’ve utilized, such as scalability and performance analysis, and how those benefits have impacted your data projects positively.

Narrate a specific project that involved data modeling, articulating your role, the challenges faced, and the outcomes achieved. Highlight any specific methodologies used and the skills you applied.

Talk about your commitment to continuous learning through webinars, courses, and industry conferences. Mention any certifications or latest technologies you’re currently exploring to stay ahead in your field.

Focus on your adaptability by discussing how you assess changing requirements, prioritize tasks, and maintain quality under pressure. Provide a past example that captures your response to abrupt changes.

Discuss the significance of having a ‘single source of truth’ for accurate and reliable data in decision-making processes. Explain how you’ve contributed to creating or maintaining it in previous roles.

Describe your experience managing workflows with Airflow, including examples of pipelines you’ve built or managed. Highlight your understanding of how orchestration improves data reliability and scheduling.