Data Engineer

As a Data Engineer at Cogstate, your key responsibilities include designing, developing, and maintaining scalable data pipelines using Azure Data Factory, managing Azure Databricks for complex data transformations, and ensuring effective data storage solutions in Azure Data Lake Storage Gen2. You'll collaborate with various teams to define data requirements and support data-driven decision-making processes.

To qualify for the Data Engineer position at Cogstate, candidates should have at least a BS/BA in Computer Science, Data Science, or a related field, along with 5+ years of data engineering experience. A minimum of 2 years working with Apache Spark and proficiency in Python or Scala are also essential, coupled with strong SQL skills for database management.

The Data Engineer role at Cogstate is crucial in optimizing the measurement of brain health by ensuring that data is efficiently collected, processed, and analyzed. By building robust data pipelines and providing reliable data solutions, you facilitate the work of clinical and data scientists in developing new medicines and drawing earlier clinical insights.

A Data Engineer at Cogstate will work primarily with Azure technologies, including Azure Databricks, Azure Data Factory, and Azure Data Lake Storage Gen2. Familiarity with tools like GitHub for source control and CI/CD processes is also essential, alongside experience in Apache Spark, Python, and SQL databases.

Cogstate embraces a virtual-first culture, allowing Data Engineers to work remotely from any location within the United States. Additionally, they provide generous paid time off, competitive health and wellness benefits, and a robust learning and development program to help you thrive in your role.

When answering this question, focus on specific projects you've worked on using Azure Databricks, highlighting your role in building data pipelines or transformations. Discuss any challenges you faced and how you overcame them, emphasizing your problem-solving skills and knowledge of best practices.

Convey your understanding of performance tuning and cost optimization strategies, such as partitioning data, reducing transformations, or using appropriate data storage solutions. Providing an example of a successful optimization you've implemented in a previous role can enhance your answer.

Emphasize your communication skills and your ability to understand the needs of different stakeholders. Discuss how you facilitate meetings to gather requirements and how you ensure that your data solutions support their research and analysis objectives effectively.

Mention the programming languages you are proficient in, such as Python or Scala, and provide examples of how you've utilized them in building data processing pipelines or developing data analysis tools. This showcases your technical expertise and adaptability.

Discuss your hands-on experience with CI/CD tools like GitHub Actions. Provide concrete examples of how you’ve implemented CI/CD pipelines to automate testing and deployment processes, illustrating how this has enhanced efficiency in your previous projects.

When answering this question, be detailed about the problem and your approach to solving it. Highlight your analytical thinking and technical skills in reasoning through the issue and employing tools or best practices to reach a solution.

Talk about the techniques you use to monitor data quality, such as data validation checks, logging, and tracking data lineage. You can discuss your familiarity with data governance frameworks and how you apply them in your pipelines to manage access and security.

It's important to articulate your specific experience with Azure Data Lake Storage, including any implementations you have worked on. Provide details about the architecture you used, data storage practices, and how you managed data accessibility and lifecycle.

Share your commitment to continuous learning and professional development. Mention any resources, communities, or platforms you follow to stay informed about trends and advancements in data engineering technologies.

Discuss your time management and organizational strategies, such as using Agile methodologies or tools like Jira. Highlight your comfort level with adjusting priorities and tackling tasks based on project needs or stakeholder input.