AWS Python Data Engineer

At CGI, the AWS Python Data Engineer is responsible for designing and implementing scalable data pipelines using various AWS services such as Glue, Lambda, and Redshift. They create real-time and batch processing solutions using Apache Spark and Kinesis while ensuring data quality through ETL processes. The role also encompasses maintaining data governance and security compliance, thereby ensuring that our vast data landscape is both accessible and secure for our clients.

To excel as an AWS Python Data Engineer at CGI, candidates should have at least 8 years of data engineering experience, with a minimum of 5 years working hands-on with AWS services. Proficiency in SQL, Python, and distributed computing frameworks like Spark is essential. Familiarity with ETL processes, data modeling, and governance—as well as experience with CI/CD pipelines—will set you apart as a top candidate for this role.

Aspiring AWS Python Data Engineers at CGI should be well-versed in AWS Glue for ETL processes, Redshift and Snowflake for data warehousing, and S3 for data storage. Experience with other services like Lambda, Step Functions, Kinesis, and EMR is highly valued, as these tools are integral to building scalable and efficient data pipelines.

CGI is deeply committed to the professional growth of its AWS Python Data Engineers through ongoing learning opportunities and tuition assistance. From mentorship programs to involvement in engaging projects, you will have the chance to expand your skill set and grow your career alongside seasoned professionals and industry leaders in a supportive environment.

The work environment at CGI for an AWS Python Data Engineer is dynamic and collaborative, emphasizing teamwork and innovation. You'll find yourself in an agile office setting that encourages open communication and ownership, allowing you to engage actively with colleagues and clients as you solve challenging problems in the data landscape.

When preparing to answer this question, provide specific examples of AWS services you've used, such as Glue for ETL, Redshift for data storage, or Lambda for serverless computing. Explain how you've utilized these services in your past projects to highlight your hands-on experience.

In your response, outline your steps for constructing data pipelines, focusing on identifying data sources, designing ETL processes, and ensuring data quality. Discuss any tools or frameworks you prefer, such as Apache Spark or Kinesis, and how they fit into your pipeline strategy.

Employ the STAR method (Situation, Task, Action, Result) while recounting a specific scenario. Discuss the complexities you encountered, the actions you took to address them, and the positive outcomes of your efforts in optimizing data processing or ensuring data integrity.

Highlight your strategies for maintaining data quality, such as implementing automated testing, conducting regular audits, or utilizing data validation frameworks. Share your familiarity with compliance standards and how you've enforced them in your previous projects.

Be prepared to discuss specific SQL queries you've written, the data sets you interacted with, and any performance challenges you overcame. Share insights into optimizing queries and ensuring efficient data retrieval, which demonstrates your analytical thinking.

Emphasize your belief in collaboration and how it shapes your workflow. Discuss examples of cross-functional projects where teamwork made a difference, highlighting your communication skills and willingness to share knowledge with others.

Articulate your understanding of data governance principles, mentioning specific frameworks or tools you've used. Share your experience with data cataloging, documentation, and ensuring compliance with organizational policies.

Detail your experience with Continuous Integration and Continuous Deployment methodologies. Discuss the tools you’ve utilized, such as GitHub Actions or CodePipeline, and how you've set up automated workflows for data projects, ensuring consistent and reliable deployments.

Stay informed about industry trends and technologies such as data lakehouses, machine learning integration, or real-time analytics. Share your excitement for how these advancements can enhance data engineering practices and ultimately benefit CGI and its clients.

Discuss your methods for managing projects under pressure, such as prioritizing tasks, effective communication with stakeholders, and employing agile methodologies. Showcase your resilience and ability to deliver high-quality work even in challenging circumstances.