Data Engineer

As a Data Engineer at Southern Company, your primary responsibilities will include designing and developing efficient data pipelines, creating robust data models, and implementing ETL processes. You'll also be tasked with working closely with both business and technical stakeholders to ensure that our solutions align with organizational strategies. Your role will involve utilizing Azure and Databricks to optimize application code and perform data quality checks, thereby supporting effective data management.

To qualify for the Data Engineer position at Southern Company, you should have a Bachelor’s degree in Computer Science, Management Information Systems, or a related technology field. Relevant experience with Databricks, Microsoft Azure, and cloud PaaS resources is essential. Additionally, familiarity with Agile methodologies and a strong understanding of data modeling, ETL processes, and data lake architectures will be important in this role.

The Data Engineer at Southern Company will work extensively with Microsoft Azure, utilizing its services like CosmosDB, Azure Data Factory, and Storage Application Services to design and develop scalable data solutions. You will implement data-oriented architectures that leverage Azure’s capabilities to optimize performance, ensure security, and reduce costs while supporting business objectives through data management.

A successful Data Engineer at Southern Company should be proficient in Databricks, particularly with its notebook, clusters, and jobs features. Familiarity with data engineering tools such as Apache Spark, Delta Lake, and MLflow will be beneficial. You should also have a good grasp of cloud platforms, especially Azure, and have experience working with structured and unstructured data across various data storage solutions.

The team culture for Data Engineers at Southern Company is one of collaboration and continuous learning. You will be encouraged to build effective relationships with peers and management, ensuring open communication about project requirements and feedback. The environment values innovation and problem-solving, making it a great place for professionals eager to grow their technical skills and contribute to meaningful projects.

In answering this question, focus on specific projects where you used Databricks for data engineering tasks. Discuss how you have utilized Databricks notebooks, clusters, and jobs to build data pipelines or perform data transformations. Highlight any challenges you faced and how you overcame them, emphasizing your problem-solving skills.

When responding, outline your systematic approach to designing a data pipeline. Discuss the importance of understanding data sources, defining transformation requirements, and ensuring data quality. Mention any specific methodologies you follow, like Agile or DevOps practices, and how you implement best practices in your pipeline construction.

In your answer, provide details about the techniques you use to improve ETL processes, such as parallel processing, incremental loading, and data partitioning. Be sure to mention how these strategies can help reduce runtime and improve overall efficiency while ensuring data quality.

Discuss your hands-on experience with specific Azure services relevant to data engineering. You could mention CosmosDB for database needs, Azure Data Factory for orchestrating data workflows, or any other services you have utilized. Highlight projects where these services played a pivotal role in your solutions.

When asked about data modeling, explain the types of data models you've worked on, whether logical or physical. Discuss the techniques you use for data normalization and the importance of ensuring that models align with business requirements and analytical needs.

In response, describe what medallion architecture is and its significance in data lakes. Explain the three layers: bronze (raw data), silver (cleaned data), and gold (enhanced, aggregated data), emphasizing how each layer serves a different purpose and improves data usability for analytics.

Share your strategies for performance tuning, such as indexing, partitioning, and using materialized views. Discuss any performance metrics you monitor and how you use that data to inform your tuning strategies. Provide examples from your past work if applicable.

Discuss your familiarity with Agile and DevOps methodologies. Explain how you implement these methodologies in your workflow, including iterative development, frequent collaboration with stakeholders, and the continuous integration and delivery of data solutions.

When answering, emphasize your understanding of data privacy regulations and best practices for securing data both in transit and at rest. Discuss encryption methods, access controls, and how you monitor for potential vulnerabilities as part of your security protocols.

Provide a specific example of a challenging project, discussing the complexities you faced, the solutions you implemented, and the impact of your work. Highlight your analytical thinking and problem-solving capabilities, as well as teamwork in overcoming the challenges.