Big Data Engineer - AWS (Flexible Hybrid)

As a Big Data Engineer - AWS at Fannie Mae, you will be responsible for designing, developing, and implementing data solutions that address customer needs. Your duties will include creating software solutions, overseeing the maintenance of existing systems, and coordinating end-to-end tasks across teams. You'll also work with big data technologies such as Hadoop and ensure that our cloud-based software operates efficiently to help us deliver on our mission.

To qualify for the Big Data Engineer - AWS position at Fannie Mae, candidates should have at least 2 years of experience with Big Data technologies like Hadoop and Spark, as well as cloud computing experience deploying applications in AWS. A background in Java, SQL, and Python is also important, along with a strong knowledge of ETL processes. While a Bachelor’s degree is preferred, equivalent experience is also considered.

Fannie Mae is committed to fostering a culture of growth and innovation, especially for roles like Big Data Engineer - AWS. We provide ongoing training and development opportunities, mentorship programs, and a collaborative environment, allowing you to thrive and expand your skill set. Our hybrid work model also lets you balance professional and personal growth in a flexible manner.

As a Big Data Engineer - AWS at Fannie Mae, you will work with a robust set of technologies, including but not limited to Hadoop, Spark, AWS services such as S3, Glue, and Lambda, as well as programming languages like Java, Python, and SQL. You will use these tools to develop solutions that enhance our ability to meet customer needs and improve access to housing finance.

Yes! Fannie Mae supports a flexible hybrid work model which allows Big Data Engineers - AWS to work remotely or in the office, depending on what suits your needs best. While we embrace flexibility, there may be times when in-person collaboration is necessary, so proximity to the Reston, VA office is preferred for this role.

In your response, highlight specific projects where you've utilized Hadoop and Spark, focusing on the frameworks you've used and the outcomes of your work. This could include discussing data pipelines you've created, performance improvements, or insights gained through analysis.

When answering this question, discuss specific AWS services you've used, such as using AWS Lambda for serverless applications or optimizing S3 storage costs. Share any metrics you've improved or processes you've put in place to enhance efficiency.

Share a structured approach to troubleshooting, such as checking system logs, validating data inputs, and using monitoring tools like AWS CloudWatch. Discuss a particular challenge you faced, how you identified the issue, and the eventual resolution.

Discuss you’re your experience designing, implementing, or optimizing ETL processes. Be specific about the tools and methodologies you utilized, how you ensured data quality, and any challenges you overcame in the process.

While it's not mandatory, demonstrating knowledge or interest in the mortgage and housing finance domain can set you apart. Mention any relevant experience or your willingness to learn and adapt to this field while showcasing your passion for the purpose behind Fannie Mae.

Outline a specific project, your role, and how you contributed to its success. Discuss collaboration methods, tools used, and how the team's combined efforts led to notable outcomes. It illustrates how well you work with others and your problem-solving skills.

Mention your proficiency in languages such as Java, Python, or SQL and describe how you've applied them in past projects to develop solutions, manipulate data, or enhance application performance. Providing real-world examples will strengthen your answer.

Convey your commitment to continuous learning by mentioning specific resources you follow, such as blogs, webinars, or industry groups. You might also highlight any recent knowledge you've gained, particularly concerning emerging tools or methodologies in big data.

Share your strategies for effective task prioritization such as using project management tools, adopting time-management techniques like the Eisenhower matrix, and ensuring open communication with team members. Emphasize your ability to adapt during peak project times.

Discuss the range of factors you take into account, including user needs, scalability, maintainability, and performance. Mention specific design methodologies or frameworks you utilize to ensure the solutions you implement meet high standards and align with business objectives.