Principal Data Engineer

As the Principal Data Engineer at Newsela, your responsibilities encompass leading data initiatives, collaborating with teams to create data system changes, and maintaining data pipelines. You'll also build and manage a data catalog, develop extensive documentation, and optimize SQL query performance. Additionally, you'll play a critical role in establishing health checks and monitoring on data systems to ensure reliability.

To qualify for the Principal Data Engineer role at Newsela, you should have a deep mastery of SQL and Python and proven experience with relational databases. Advanced skills in building data pipelines, event-based pipelines, and data testing strategies are also required. Familiarity with cloud-based infrastructure such as AWS or GCP, as well as DBT orchestration, are considered a bonus.

In the Principal Data Engineer position at Newsela, you will primarily work with SQL, Python, relational datastores, and tools for building data pipelines like Dagster or Airflow. You may also engage with monitoring tools and cloud infrastructure using platforms such as AWS and GCP, along with Terraform for infrastructure as code.

Absolutely! The Principal Data Engineer role at Newsela is fully remote, allowing you to work from anywhere while enjoying the professional flexibility and work-life balance that comes with our inclusive company culture.

At Newsela, we prioritize your professional growth as a Principal Data Engineer by offering holistic learning and development programs. You will have access to tailored individual development plans, community learning sessions, and opportunities that foster a sense of belonging while honing your skills and competencies.

When answering this question, share specific examples of data pipelines you've designed and built. Highlight the technologies you used, the challenges you faced, and how you optimized the pipelines for performance and reliability.

Discuss your strategies for SQL optimization, such as indexing, analyzing query execution plans, and avoiding unnecessary complexity in queries. Provide a specific instance where your optimization led to measurable performance improvements.

Be prepared to discuss any tools you have experience with, such as DBT, Apache Airflow, or Dagster. Explain your rationale for using these tools and how they improved your workflows and data management.

Emphasize the importance of implementing robust data testing strategies. You can discuss techniques like unit tests, integration tests, and continuous monitoring to validate data integrity throughout the pipeline.

Ensure you share specific projects where you utilized AWS or GCP for data infrastructure tasks. Discuss how you managed cloud services, set up data storage, and orchestrated workflows ensuring efficiency and cost-effectiveness.

When discussing challenging situations, be specific about the problem, your analysis, the solution you proposed, and the resulting outcome. This showcases your problem-solving skills and your ability to handle pressure.

Share your understanding of what a data catalog is, why it’s important, and your experience in setting one up. Discuss how you’ve worked with metadata and how it improved data discovery and accessibility for your team.

Talk about your communication approach, emphasizing the importance of aligning with various teams and stakeholders to gather clear requirements. Provide examples of successful collaborations that led to effective solutions.

Discuss your hands-on experience managing large-scale data migrations. Highlight specific techniques or tools you employed, and any challenges you encountered along the way, and how you overcame them.

Don’t hesitate to share your favorite resources such as online courses, webinars, or industry publications. Mention how you engage with communities and networks to keep abreast of new technologies and best practices in data engineering.