AWS Data Engineer Resume Example & Writing Guide

Use this AWS Data Engineer resume example and guide to improve your career and write a powerful resume that will separate you from the competition.

If you’re interested in working with big data, but aren’t sure where to start, becoming a data engineer might be the perfect job for you. Data engineers work with large datasets to collect insights and build solutions that help companies run more efficiently. They use their coding skills to create tools that automate tedious tasks and free up their coworkers to focus on more important things.

Data engineers often work closely with data analysts and data scientists to identify trends and create reports. They might also collaborate with product managers to help develop new features or build tools that improve user experience. And because data engineers work in such a diverse field, they have the opportunity to work on a variety of different types of projects.

Here are some tips and an example to help you write a fantastic data engineer resume that will get you noticed by recruiters.

Jennifer Thomas
Chicago, IL | (123) 456-7891 | [email protected]

Experienced data engineer with a passion for data-driven decision making. Proven ability to develop and manage big data pipelines, build data models, and perform statistical analysis. Seeking an opportunity to use big data to solve real-world problems and improve the lives of others.

Northeastern University Jun '10
M.S. in Computer Science
Northeastern University Jun '07
B.S. in Computer Science
Company A, AWS Data Engineer Jan '17 – Current
  • Developed and implemented data pipelines using AWS services such as Kinesis, S3, EMR, Athena, Redshift to process petabyte-scale data in real time.
  • Designed and developed scalable solutions for storing and processing large amounts of data across multiple regions.
  • Analyzed the business requirements and translate them into technical specifications that can be used by developers to implement new features or enhancements.
  • Provided support during all phases of development including design, implementation, testing, deployment and maintenance of applications/services.
  • Participated in cross-functional teams (e.g., infrastructure engineering) when required to ensure effective communication between groups with overlapping functionality or shared resources.
Company B, AWS Data Engineer Jan '12 – Dec '16
  • Implemented a data warehouse using Redshift to store and analyze terabytes of raw data
  • Built ETL processes in Python, Pig, and SQL to transform unstructured data into structured datasets
  • Developed an automated machine learning system that reduced manual labor by 80%
  • Created custom dashboards with Tableau for real-time monitoring of key business metrics
  • Spearheaded the migration from on-premise servers to AWS cloud infrastructure (EC2, S3, RDS)
Company C, Data Analyst Jan '09 – Dec '11
  • Conducted data analysis to support business decision-making by extracting, cleansing, and manipulating data from various sources.
  • Created data visualizations to communicate complex data sets in an easily understandable format for business users.
  • Developed and maintained reporting dashboards to track KPIs and other business metrics.
  • AWS Certified Solutions Architect – Associate
  • AWS Certified Developer – Associate
  • AWS Certified SysOps Administrator – Associate

Industry Knowledge: AWS, Linux, AWS Security, AWS RDS, AWS CloudFormation, AWS S3, AWS EC2, AWS Lambda, AWS IAM
Technical Skills: Amazon Redshift, Amazon DynamoDB, Amazon CloudFront, Amazon Route 53, Amazon Glacier, Amazon SNS, Amazon SimpleDB, Amazon SQS
Soft Skills: Communication, Attention to Detail, Problem Solving, Teamwork, Leadership, Self-Motivation, Self-Learning

How to Write an AWS Data Engineer Resume

Here’s how to write an resume of your own.

Write Compelling Bullet Points

The best resumes are clear and concise. They focus on the most relevant experience and bullet points, and they use specific language to describe projects and responsibilities.

For example, rather than saying you “managed data for large-scale analytics platform,” you could say you “managed data for Amazon Web Services (AWS) data analytics platform, providing insights for over 1,000 users across six departments.”

The second bullet point is much stronger because it provides more detail about the project and the results of your work. It also mentions the name of the company and the number of people who used the platform, which makes the project seem even more impressive.

Identify and Include Relevant Keywords

When you apply for a job as a data engineer, your resume is usually scanned by an applicant tracking system (ATS) for certain keywords. If your resume doesn’t include enough of the right keywords, your application might not make it past the initial screening process.

That’s why it’s important to include keywords in your resume. You can find these keywords by reading through the job posting and including terms that are repeated in your resume.

  • Amazon Web Services (AWS)
  • Python (Programming Language)
  • Git
  • SQL
  • Unix
  • Bash
  • Data Engineering
  • Machine Learning
  • R (Programming Language)
  • Databases
  • JavaScript
  • Apache Spark
  • Data Analysis
  • Scala
  • Data Science
  • Hadoop
  • Linux
  • AWS Lambda
  • Agile Methodologies
  • Algorithms
  • Big Data
  • MySQL
  • Microsoft SQL Server
  • Scrum
  • Shell Scripting
  • Amazon EC2
  • Microservices
  • DevOps
  • HTML5
  • TypeScript

Showcase Your Technical Skills

As an AWS data engineer, you need to be proficient in a variety of software programs and systems in order to effectively manage and process data. Some of the most commonly used programs and systems include Amazon S3, Amazon EMR, Amazon Athena, and Amazon Redshift. Additionally, you should have a solid understanding of big data concepts and be able to effectively use tools like Hadoop, Hive, and Spark.


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