Data Engineer

As a Data Engineer at our company, you’ll be tasked with designing scalable data architectures tailored for LLM use cases. Your responsibilities include developing and maintaining data pipelines that pull data from various sources into a vector database in AWS, implementing role-based access control, and monitoring data processes. You'll present significant findings to stakeholders, engaging collaboratively within our Agile team to achieve project goals.

To apply for the Data Engineer position at NVM, you should possess strong expertise in SQL, Python, and cloud environments—AWS is preferred. Familiarity with both structured and unstructured databases, vector databases, data orchestration tools, and Agile/Scrum methodologies is critical. Excellent communication skills in English and experience designing complex data architectures are also essential qualifications for this role.

At our company, Data Engineers enjoy a hybrid work model, spending 3 days in the office and 2 days working remotely. This setup allows for flexibility while maintaining strong team collaboration. We pride ourselves on a positive culture that encourages learning, innovation, and teamwork. You’ll also benefit from 37 vacation days, ensuring a healthy work-life balance.

In your role as Data Engineer, you’ll support the AI assistant project by creating and managing the data infrastructure that powers AI applications. You'll develop and optimize data pipelines to ensure accurate data flow, implement data security protocols, and work closely with LLM engineers to transform prototypes into functioning solutions. Your contributions will be key to the successful deployment of our AI assistant.

Currently, our company does not offer visa sponsorship or relocation assistance for the Data Engineer position. We focus on building a diverse and talented team, but candidates are expected to reside within the region or be prepared to work under a hybrid model without relocation support.

When answering this question, focus on specific projects where you've designed and implemented data architectures. Highlight your familiarity with both structured and unstructured data along with the tools you've used, such as AWS. Discuss how you handled scalability and what challenges you faced during the process.

Address this question by mentioning the practices you implement to ensure data integrity, such as data validation rules, regular audits, and error handling mechanisms. Provide examples from your experience where you successfully identified and rectified data quality issues.

List the data orchestration tools you are familiar with, like Apache Airflow or AWS Data Pipeline, and explain how you've utilized them in past projects. Be ready to discuss specific scenarios where these tools enhanced your workflow or solved a problem.

Describe your collaborative process, emphasizing clear communication and teamwork. Provide an example of a project where you worked closely with engineers, detailing your strategies for effective collaboration and any tools you used for project management.

Explain your approach to continuous learning—this could include following industry publications, attending webinars or conferences, and participating in relevant online communities. Mention any specific resources you find valuable and how they’ve influenced your work.

Clarify your understanding of role-based access control (RBAC) by defining it as a method of restricting system access to authorized users based on their roles. Share any experiences where you implemented RBAC in a project, discussing how it enhanced data security.

Choose a specific project where you faced significant challenges such as scaling issues or data source integration complexities. Discuss the obstacles encountered, your problem-solving approach, and the successful outcome that resulted from your efforts.

Talk about your systematic approach to incident management, including identifying, diagnosing, resolving, and documenting incidents. Share any relevant experiences where your timely management minimized downtime or data loss.

List the tools and technologies you are experienced with, such as Grafana, Prometheus, or AWS CloudWatch. Discuss how you leverage these tools to track performance metrics and optimize your data processes, providing examples of improvements made.

Discuss your experience working in Agile methodologies, emphasizing your role in sprints, daily stand-ups, and sprint planning. Share specific instances where working Agile improved project outcomes or team dynamics.