AWS Data Engineer with Python

As an AWS Data Engineer at LSEG, you will be responsible for designing and maintaining robust ETL pipelines using Python and Spark. Your duties will include developing workflows with AWS services such as EMR Serverless, Glue, and S3, as well as ensuring data quality and managing governance frameworks. You'll collaborate across teams to gather requirements and contribute to technical discussions while mentoring junior engineers.

To qualify for the AWS Data Engineer role at LSEG, it's essential to have expertise in Python and Spark, with hands-on experience in AWS services like EMR, Glue, and Lambda. A strong understanding of data quality frameworks and a practical knowledge of Apache Iceberg for data lakehouse solutions will further strengthen your application. Familiarity with agile methodologies and excellent communication skills are also crucial.

At LSEG, AWS Data Engineers have ample opportunities for career growth. In this role, you can step into leadership positions while continuing to work with innovative technologies. By mentoring junior team members and contributing to tech discussions, you can expand your skill set and take on more responsibilities. Working in a dynamic and supportive environment also enables you to grow both personally and professionally.

The work culture for AWS Data Engineers at LSEG is dynamic and inclusive. The company encourages collaboration and innovation, valuing each employee's unique contributions. In addition to a supportive atmosphere, LSEG promotes sustainability and is committed to creating a diverse workforce. You'll have the chance to express your individuality while working towards shared goals.

LSEG is dedicated to the professional development of its employees, including AWS Data Engineers. The company provides tailored learning opportunities, access to training programs, and mentoring resources to help you enhance your technical skills and leadership capabilities. From hands-on workshops to collaborative projects, LSEG ensures that your professional growth is both supported and encouraged.

In response to this question, discuss specific projects where you designed ETL workflows using Python and Spark. Provide details about the challenges you faced, the solutions you implemented, and the outcomes achieved. This demonstrates your technical expertise and problem-solving abilities, which are key traits for the AWS Data Engineer role.

To answer this question effectively, talk through the data quality frameworks and governance practices you follow. Discuss specific methods like data validation, cleansing processes, or monitoring systems you implement to maintain high data quality throughout your ETL pipelines. Providing examples will make your answer more impactful.

This question gives you a chance to showcase your analytical skills. Outline the specific pipeline you worked on, the bottlenecks you identified, and the optimization strategies you applied. Discuss the technologies used, and highlight how your actions improved performance and efficiency.

Outline your communication strategies and how you tailor your technical vocabulary to fit different audiences. Provide examples of how you've effectively gathered requirements from stakeholders, provided insights during technical discussions, and ensured that the project meets various team needs, reinforcing your teamwork skills.

Discuss specific AWS services you've worked with, such as EMR, Glue, and S3. Talk about your hands-on experience with these tools in your ETL processes, highlighting how they contributed to the success of projects and improved data management. Concrete examples will demonstrate your expertise.

Tailor your answer to reflect your understanding of audience needs. Emphasize your ability to break down complex ideas into simpler terms, possibly using analogies or visual aids. Share an instance where you've successfully communicated a complex concept to a non-technical team member, ensuring they left with a clear understanding.

In your answer, explain Apache Iceberg as an important layer in optimizing data lakes. Discuss how it helps enforce schema evolution and manage data efficiently. Providing examples of how you've utilized Iceberg in previous projects can give your answer depth and relevance to the AWS Data Engineer position.

This question provides an opportunity to share your approach to continuous learning. Talk about resources you utilize, such as online courses, forums, or collaborative projects, and highlight how you apply newly acquired knowledge to improve your work. This exhibits your commitment to professional development.

This is your chance to demonstrate your problem-solving skills. Describe the technical problem in detail, the steps you took to resolve it, and the impact of your solution on the overall project. Be concise yet thorough, showcasing your analytical abilities.

Discuss your experience with agile methodologies such as Scrum or Kanban. Explain the practices you follow in sprint planning, retrospectives, and daily stand-ups, highlighting how they enhance team collaboration and project delivery. This will showcase your familiarity with agile practices, which are essential in a dynamic environment like LSEG.