Senior Data Engineer

As a Senior Data Engineer at New Relic, your key responsibilities include leading the design and construction of scalable data pipelines, ensuring robust data quality measures are in place, and collaborating with numerous teams. You will also significantly influence architectural design, improve engineering processes, and provide thought leadership in evolving our Data Engineering function.

Candidates looking to become a Senior Data Engineer at New Relic should have a minimum of 5 years of experience in Business Intelligence and Data Warehousing. You should be adept with AWS tools and have strong coding skills in languages like Python, R, or Scala. Knowledge of ETL/ELT patterns and SQL are also imperative for success in this role.

Yes! At New Relic, Senior Data Engineers work with a variety of tools including AWS components like Spark, Glue, and Athena. Familiarity with dbt, Airflow, and Snowflake, as well as experience with data observability and Apache Iceberg tables, will certainly give you a competitive edge.

New Relic prides itself on fostering a diverse and inclusive work culture. As a Senior Data Engineer, you will be part of a welcoming environment that encourages everyone to express their authentic selves while contributing their unique insights. The company values collaboration and continuous improvement at all levels.

Absolutely! New Relic embraces a flexible workforce model, providing opportunities for fully remote, fully office-based, or hybrid working arrangements. This flexibility ensures that as a Senior Data Engineer, you can work in the environment that best supports your success.

When answering, highlight specific projects where you designed and executed data pipelines. Focus on your role in ensuring fault tolerance and data quality, and mention any challenges you faced and how you overcame them.

Share your methodology for maintaining data quality. Discuss specific practices such as implementing rigorous testing, using data validation tools, and creating monitoring frameworks to catch discrepancies early.

Discuss the specific AWS tools you’ve used, such as Spark or Athena. Explain how you integrated these tools into your data engineering projects, emphasizing the impact they had on performance and efficiency.

Convey your understanding of cross-functional teamwork. Share examples of how you actively seek to understand the needs of Data Scientists and Product Managers and integrate that feedback into your data solutions.

Reflect on emerging trends and technologies that excite you in the field of data engineering. Express how you plan to stay ahead of industry trends and ensure your skills remain relevant.

Provide a specific example, explaining the context of the challenge, your role, the actions you took, and the outcome. Focus on problem-solving and leadership skills.

Discuss your practical experience with ETL and ELT processes. Provide examples of the tools you’ve used and how you chose an approach based on project requirements.

Speak to your experience with specific data observability tools and frameworks. Explain how you’ve implemented them to monitor data pipelines and ensure data integrity.

Prepare to share a detailed account of the initiative, emphasizing your leadership role, the team dynamics, and the measurable outcomes that demonstrated success.

Identify your strongest programming languages and describe your proficiency with each. Provide examples of how you’ve used them in past projects and any relevant frameworks or libraries.