Azure Databricks Engineer

As an Azure Databricks Engineer at MSRcosmos, your primary responsibilities include managing and securing data assets within the Databricks ecosystem using Unity Catalog, implementing monitoring and observability measures with Datadog, processing data through Python and Pyspark scripts, and collaborating with version control systems like GitHub. Additionally, you will be involved in automating workflows using GitHub Actions and configuring CI/CD pipelines, all while leveraging modern AI coding assistants.

To be a strong candidate for the Azure Databricks Engineer role at MSRcosmos, you should possess hands-on experience with data management in Databricks, strong knowledge of Python and Pyspark for data processing, and familiarity with DevOps practices. Furthermore, a deep understanding of dimensional data modeling, experience with CI/CD practices, and knowledge of monitoring tools like Datadog are essential.

The Azure Databricks Engineer position at MSRcosmos is day one onsite in Cincinnati, Ohio. This allows for greater collaboration with your team and hands-on engagement with the Databricks environment, facilitating a better understanding of the architecture and data assets.

In your role as an Azure Databricks Engineer at MSRcosmos, you will utilize a variety of tools and technologies including Databricks for data management and analysis, Python and Pyspark for coding, GitHub for version control, Datadog for monitoring, and other AI coding assistants. Your work will also incorporate automation workflows and CI/CD pipeline tools.

MSRcosmos is committed to supporting the professional growth of its Azure Databricks Engineers by offering opportunities for skill advancement through access to the latest technologies, collaborative work environments, and a culture that emphasizes continuous learning and development. You'll be encouraged to stay updated on industry trends and innovations.

In answering this question, focus on the different components of the Databricks architecture, such as clusters, notebooks, and jobs. Highlight any recent experiences you've had with implementing or using specific features within the Databricks environment to provide a detailed perspective on your expertise.

Discuss your approach towards managing and securing data assets, emphasizing the use of Unity Catalog for governance as well as setting up access permissions and working with data encryption to ensure data integrity and security throughout the Databricks ecosystem.

When discussing your CI/CD experience, be sure to describe any specific tools you've used for pipeline automation, such as GitHub Actions. Explain how you've configured these pipelines to streamline deployments and maintain version control, ensuring that code changes are continuously integrated and delivered.

Illustrate your proficiency in Python and Pyspark by providing examples of projects where you utilized these tools for processing large datasets or performing complex data transformations. Highlight any specific libraries or techniques you've implemented to optimize performance.

In your response, stress the importance of monitoring and observability in maintaining system health. Discuss tools like Datadog that you've used, and how real-time monitoring can help detect anomalies and improve overall reliability and efficiency of data processing.

Explain how you leverage GitHub Actions to create automated workflows, such as triggering jobs or workflows based on certain events. Provide a brief overview of how automation can lead to increased efficiency and reduce manual intervention in Databricks processes.

When answering this question, define dimensional data modeling and discuss how it is applied in data warehousing and analytics. Share a specific experience where you designed or utilized dimensional models to support reporting or analytics endeavors.

Discuss the benefits of using AI coding assistants like GitHub Copilot and Databricks Assistant in your daily workflow. Focus on how these tools enhance productivity, reduce errors, and guide you through coding challenges, ultimately streamlining your engineering tasks.

Emphasize the importance of systematic version control and how you utilize GitHub for collaboration. Facts such as branching strategies, commit messages, and merge processes should be highlighted to illustrate your dedication to maintaining organized and trackable code.

Use this opportunity to share a specific case where you encountered a challenging data processing issue in Databricks. Describe the steps you took to address the problem, highlighting any innovative solutions you implemented to achieve successful results.