Senior Data Engineer Core

As a Senior Data Engineer at Viz.ai, your primary responsibilities include designing and building next-generation data infrastructure and pipelines. You will contribute to the development of our core Data Platform, design complex data pipelines, and collaborate closely with various teams to support our data products. Your role will focus on creating reliable and scalable solutions that serve both our customers and internal teams.

To qualify as a Senior Data Engineer at Viz.ai, you should have a minimum of 5 years of professional experience in data engineering, with at least 2 years of hands-on experience using Python or similar programming languages. A strong background in SQL and NoSQL databases is essential, along with experience in data pipeline architecture, ETL, and ELT projects. Candidates with a B.Sc. or higher in Computer Science or a related field will be well-suited for this role.

At Viz.ai, our tech stack includes a variety of tools designed to enhance our data engineering capabilities. We utilize AWS for cloud services, Python as our core programming language, and technologies like DBT, ETL, ELT, DynamoDB, Postgres, and RabbitMQ. We value innovation and are open to exploring new tools that can improve our workflows.

Viz.ai is dedicated to fostering a culture of growth and learning. As a Senior Data Engineer, you will have opportunities to mentor junior team members, contribute to code reviews, and participate in initiatives that align with your interests. Our collaborative environment encourages you to propose new ideas and continue developing your skills, ensuring that you can advance your career while contributing to meaningful projects.

The company culture at Viz.ai is dynamic, innovative, and team-oriented. We believe that our people are the heart of everything we do, and we celebrate collaboration among various teams. As a Senior Data Engineer, you’ll find an environment that supports autonomy, encourages knowledge sharing, and values your contributions toward our mission of transforming healthcare through technology.

In your response, emphasize specific projects where you designed data pipelines, including the technologies used, the challenges faced, and how you overcame them. Discuss how you ensure the pipelines are scalable and reliable while also mentioning any collaboration with other teams.

Discuss your experience using AWS services like S3, DynamoDB, and Glue. Provide examples of projects where you leveraged these services for data storage, processing, or analysis. Highlight how utilizing AWS has improved your data infrastructure solutions.

Highlight your time management and prioritization skills, explaining any frameworks or tools you use to keep track of your tasks. Provide an example of a time when you successfully balanced multiple projects and met deadlines without compromising quality.

Emphasize the importance of clear communication, regular check-ins, and understanding the goals of other teams. Discuss any past experiences where effective collaboration led to successful outcomes in your data engineering projects.

Discuss how you view code reviews as a learning opportunity for both you and your team. Share your process for providing constructive feedback and mentoring junior engineers while ensuring code quality and best practices are maintained.

Share specific challenges you have encountered, such as working with old systems or integrating new technologies. Discuss the steps you took to address these challenges and the resulting impact on the projects and teams you worked on.

Describe your methods for keeping updated with the latest trends, such as following influential blogs, attending webinars, or participating in forums. Explain how you have applied new knowledge to your work to enhance your engineering practices.

Discuss the importance of data quality in ensuring reliable insights and decisions. Share strategies you implement for data validation, cleansing, and monitoring to maintain high-quality datasets in your projects.

Explain your design principles for creating scalable data solutions, focusing on modular architecture, efficient resource use, and validation processes. Use examples from your experience to show how you achieved scalability and maintainability in past projects.

This is a great chance to express your passion for healthcare technology and how it aligns with your career goals. Discuss how the opportunity to work on impactful solutions that save lives motivates you and how you hope to contribute positively to the company culture.