As a Sr Data Engineer at GE Aerospace, your primary responsibilities will include designing and implementing data engineering processes, performing data analysis, and ensuring the quality of data through various checks. You will be tasked with building both logical and physical data models, developing ETL jobs based on comprehensive data mapping specifications, and maintaining necessary metadata structures for future data solutions.

To qualify for the Sr Data Engineer role at GE Aerospace, candidates should hold a Bachelor’s degree with at least 2 years of experience in data engineering. Alternatively, an Associate's degree with 5 years of experience, or a High School Diploma combined with 7 years of professional experience is acceptable. Proficiency in SQL and a solid understanding of data modeling tools and ETL processes are essential.

Candidates for the Sr Data Engineer role at GE Aerospace should showcase technical expertise in using industry-standard data modeling tools like ERWin or ER Studio, and have experience with ETL tools such as Informatica or Talend. Familiarity with programming languages such as Java, Python, or Scala is also highly beneficial, along with hands-on experience in Big Data technologies like Hadoop and Spark.

At GE Aerospace, the work environment for a Sr Data Engineer is collaborative and innovative. You will be part of a multi-disciplinary team, engaging closely with UX, business teams, and data professionals. The atmosphere encourages teamwork and supports professional development, fostering an open space for sharing ideas and resolving challenges.

GE Aerospace is committed to the professional development of its employees, including Sr Data Engineers. Opportunities might include training programs, mentorship, workshops, and access to cutting-edge resources, ensuring you can grow your skills and advance in your career path.

When answering this question, focus on specific data modeling tools you have used, such as ERWin or ER Studio. Discuss projects where you utilized these tools to create models that improved data handling for organizations, and explain your understanding of how effective data models can enhance data quality and accessibility.

It's important to demonstrate your analytical skills. Discuss your approach to identifying data quality issues, including the tools or techniques you use for profiling and cleaning data. Provide an example of a time you resolved these issues and the impact it had on the overall data integrity of a project.

For this question, share details about the ETL tools you have used, such as Informatica or Talend. Describe a specific project where you developed ETL processes, discussing the challenges faced and how you overcame them to deliver timely results.

Use this opportunity to showcase your problem-solving abilities. Share the context of the project, the challenges you faced, and the solutions you implemented. Highlight how your contributions led to improvements in data handling and the benefits realized by the business.

Mention resources you use to stay updated, such as industry journals, webinars, or online courses. Discuss any professional communities you engage with, and explain how being informed shapes your work as a Sr Data Engineer.

Be specific about the big data technologies you've worked with, such as Hadoop, Spark, or NoSQL databases. Explain how you have used these technologies in practical scenarios to handle large datasets and perform advanced analytics.

Illustrate your ability to collaborate by sharing examples from past experiences. Discuss how you effectively communicate and align with teams across different functions, emphasizing your teamwork skills and ability to find common ground.

When discussing data profiling, explain the methods or tools you use to analyze current data sets. Enumerate the steps taken to ensure data accuracy and integrity, such as assessing data distributions, uncovering inconsistencies, and documenting metadata.

Highlight your understanding of MDM and its role in ensuring data consistency across an organization. Provide examples of how MDM practices have benefited data quality and accessibility in your previous roles, linking it back to your work as a data engineer.

Discuss your experience with tools like Alation or Collibra if applicable. Explain how these tools can enhance data governance and streamline data accessibility, and how they fit into broader data architecture strategy.