Fannie Mae

Big Data Engineer - AWS

Fannie Mae

Reston, VA, USA
Full-TimeDepends on ExperienceSenior LevelMasters
Job Description

Fannie Mae seeks a highly motivated and technically proficient Big Data Engineer - AWS to join our talented team of data engineers. In this role, you will have the opportunity to design, build, and maintain cutting-edge big data solutions. You will have the chance to work on complex projects and use the latest AWS technologies to design and develop powerful data pipelines. The ideal candidate will be a self-starter with a passion for big data engineering and experience working in an AWS environment.To be successful in this role, you must have:• Bachelor's degree in Computer Science, Engineering, or a related field• At least 5 years of experience with developing and deploying big data solutions• Expert-level knowledge of AWS services such as S3, Athena, EMR, and DynamoDB• In-depth knowledge of distributed systems, data analysis, and software development principles• Experience with programming languages such as Java, Python, and Scala• Familiarity with open-source big data platforms such as Apache Spark and Hadoop• Excellent written and verbal communication skillsIf you are a big data enthusiast who is eager to work in an exciting and innovative environment, we want to hear from you!

Responsibilities:

  1. Design, build, and maintain big data solutions using AWS technologies.
  2. Develop and deploy powerful data pipelines.
  3. Troubleshoot and resolve issues with big data solutions.
  4. Work with programming languages such as Java, Python, and Scala.
  5. Implement open-source big data platforms such as Apache Spark and Hadoop.
  6. Monitor and optimize data flows for maximum efficiency.
  7. Collaborate with team members to ensure successful project completion.
  8. Stay up-to-date with the latest AWS innovations and industry best practices.
  9. Provide technical guidance and support to other team members.
  10. Document processes and procedures for other data engineers.
Where is this job?
This job is located at Reston, VA, USA
Job Qualifications
  • Bachelor's Degree In Computer Science Or A Related Field

  • Expertise In Programming Languages Such As Java

  • Python

  • Scala

  • And/Or R

  • Experience With Cloud-Based Solutions Such As Amazon Web Services (Aws)

  • Proficiency In Data Modeling

  • Etl

  • And Data Warehousing

  • Knowledge Of Big Data Technologies Such As Hadoop

  • Spark

  • And Kafka

  • Understanding Of Data Analysis Concepts And Techniques

Required Skills
  • Sql

  • Python

  • Java

  • Data Analysis

  • ETL

  • Machine Learning

  • Hadoop

  • Cloud

  • Kafka

  • AWS

  • Spark

  • Data Mining

  • Data Visual

Soft Skills
  • Communication

  • Leadership

  • Time management

  • Interpersonal Skills

  • self-motivation

  • Critical thinking

  • Creative Thinking

  • Teamwork

  • Adaptability

  • Problem-Solving

Compensation

According to JobzMall, the average salary range for a Big Data Engineer - AWS in Reston, VA, USA is between $90,000 and $150,000 per year. This range may vary depending on experience, location, and type of role.

Additional Information
Fannie Mae is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
Required LanguagesEnglish
Job PostedAugust 21st, 2023
Apply BeforeMay 22nd, 2025
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About Fannie Mae

The Federal National Mortgage Association, commonly known as Fannie Mae, is a United States government-sponsored enterprise and, since 1968, a publicly traded company. It engages in the provision of access to affordable mortgage credit and indirectly enables families to buy, refinance or rent homes. It operates through the following segments: Single-Family Credit Guaranty, Multifamily, and Capital Markets.

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