Data Engineer (Indexation)

As a Data Engineer (Indexation) at P2P.org, your main responsibilities include extracting and processing data from various systems, managing complex data pipelines, troubleshooting issues, and collaborating with cross-functional teams to define data requirements. You’ll also need to ensure data consistency and security throughout the integration process, continuously improving data processes to align with the needs of the business.

To excel as a Data Engineer (Indexation) at P2P.org, you should have a strong knowledge of Python and SQL, with familiarity in systems like BigQuery, Clickhouse, and Postgres being preferable. These skills will be critical for data extraction and processing tasks as part of your job.

Yes, production experience with Kafka is one of the key requirements for the Data Engineer (Indexation) role at P2P.org. This experience will enable you to effectively manage and maintain the complex data pipelines essential for our operations.

At P2P.org, you can expect a positive and friendly communication culture. We pride ourselves on creating an environment where collaboration and support among team members are encouraged. Our focus on professional growth and well-being ensures that everyone feels valued and motivated to contribute to our mission in decentralized finance.

For the Data Engineer (Indexation) position at P2P.org, you should have strong knowledge in Python and SQL, experience with Kafka and Kubernetes, and a general understanding of Google Cloud Platform (GCP), including Cloud SQL and VM. Additionally, a B2+ English level is expected to facilitate communication within the diverse team.

When answering this question, be specific about your past experiences with data extraction and processing, including tools and technologies you have utilized. Highlight any complex data pipelines you have managed and how your efforts improved data flows within previous organizations.

Provide concrete examples of how you have implemented data validation checks, monitoring, and automated testing in your projects. Emphasize your understanding of best practices for maintaining high data quality in data engineering.

Discuss your systematic approach to identifying and diagnosing data pipeline issues. You can highlight tools you use for monitoring and debugging, how you analyze logs, and any methods you apply to resolve problems efficiently.

Explain any specific projects or tasks where you have utilized GCP services, outlining your proficiency with Cloud SQL, storage solutions, and any other relevant capabilities. Be sure to connect how this experience benefits your work at P2P.org.

Share a story where you worked with different teams to deliver a project successfully. Detail the communication strategies you utilized, any challenges encountered, and how those challenges were overcome leading to a successful outcome.

Discuss your methods for staying informed about advancements in data engineering, such as following industry blogs, attending webinars, or participating in forums. Highlight any communities you are part of that help enhance your knowledge of the field.

Clearly outline how you’ve leveraged Python in your past projects, detailing the libraries you used and specific tasks you accomplished. Your answer should demonstrate your versatility and competence as a Python programmer in data engineering contexts.

Be open about past challenges you encountered with data pipelines, explaining how they arose and the creative solutions you devised. This shows your problem-solving abilities and resilience in the face of difficulties.

Articulate your passion for decentralized finance and how the mission of P2P.org aligns with your personal values. Mention specific projects or achievements of P2P.org that inspire you and outline how you hope to contribute to the team.

Explain your understanding of aligning data projects with business objectives. Discuss past examples where your data engineering efforts directly impacted business performance, showing that you can think strategically from a data perspective.