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AWS Data Engineer Resume Example

by
Kayte Grady
Reviewed by
Trish Seidel
Last Updated
June 12, 2025
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AWS Data Engineer Resume Example:

William Kim
[email protected]
•
(233) 719-4485
•
linkedin.com/in/william-kim
•
@william.kim
•
github.com/williamkim
AWS Data Engineer
Accomplished AWS Data Engineer with over 8 years of expertise in architecting scalable cloud solutions and optimizing data pipelines. Proficient in leveraging AWS Lambda and Redshift, achieving a 30% reduction in data processing time. Specializes in machine learning integration, driving innovation and team success in dynamic environments.
WORK EXPERIENCE
AWS Data Engineer
09/2023 – Present
CloudWorks
  • Led a cross-functional team to design and implement a serverless data pipeline using AWS Lambda and Kinesis, reducing data processing time by 40% and cutting operational costs by 25%.
  • Architected a scalable data lake solution on AWS S3, integrating with AWS Glue and Athena, which improved data accessibility and query performance by 50% for over 100 users.
  • Mentored a team of junior data engineers, fostering a collaborative environment that resulted in a 30% increase in project delivery speed and enhanced team skillsets in AWS technologies.
Data Engineer
04/2021 – 08/2023
DataSphere LLC
  • Optimized ETL processes using AWS Glue and Redshift, resulting in a 60% reduction in data processing time and a 20% decrease in storage costs.
  • Developed a real-time analytics dashboard using AWS QuickSight, providing stakeholders with actionable insights and enabling data-driven decisions that increased revenue by 15%.
  • Collaborated with data scientists to deploy machine learning models on AWS SageMaker, improving predictive accuracy by 35% and enhancing customer personalization strategies.
AWS Engineer
07/2019 – 03/2021
Data Dynamics Inc.
  • Implemented a data ingestion framework using AWS Data Pipeline, automating data collection from multiple sources and reducing manual data entry errors by 70%.
  • Streamlined data storage solutions by migrating legacy systems to AWS RDS, achieving a 50% improvement in data retrieval speeds and enhancing system reliability.
  • Assisted in the deployment of a cloud-based data warehouse on AWS Redshift, supporting business intelligence initiatives and improving reporting capabilities by 40%.
SKILLS & COMPETENCIES
  • Real-Time Manufacturing Data Pipeline Architecture
  • Industrial IoT Data Integration and Processing
  • Data Lake and Data Warehouse Design
  • Manufacturing Analytics and KPI Development
  • Predictive Maintenance Data Strategy
  • Supply Chain Data Optimization
  • Edge Computing Data Processing
  • Amazon Redshift
  • Apache Kafka
  • AWS Glue
  • Terraform
  • Apache Airflow
  • Generative AI for Manufacturing Intelligence
COURSES / CERTIFICATIONS
Education
Bachelor of Science in Computer Science
2016 - 2020
Carnegie Mellon University
Pittsburgh, PA
  • Data Science
  • Machine Learning
Build your AWS Data Engineer resume

What makes this AWS Data Engineer resume great

Building scalable, cost-effective data pipelines is crucial for AWS Data Engineers. This resume highlights success with serverless solutions, real-time analytics, and machine learning integration. Strong metrics back up impact on cloud cost and complexity. It also shows leadership through mentoring and accelerating project delivery. Clear and results-driven.

AWS Data Engineer Resume Template

Contact Information
[Full Name]
[email protected] • (XXX) XXX-XXXX • linkedin.com/in/your-name • City, State
Resume Summary
AWS Data Engineer with [X] years of experience architecting and implementing scalable data solutions using [AWS services] and [programming languages]. Expertise in designing [data pipeline types] and optimizing [database technologies] for big data processing. Reduced data processing time by [percentage] and improved data accuracy by [percentage] at [Previous Company]. Seeking to leverage cloud-native data engineering skills to drive data-driven innovation and enhance analytics capabilities for [Target Company] through robust, efficient, and cost-effective AWS-based data infrastructure.
Work Experience
Most Recent Position
Job Title • Start Date • End Date
Company Name
  • Architected and implemented [specific data pipeline] using AWS Glue, reducing data processing time by [X%] and improving data quality by [Y%] for [business unit/process]
  • Led migration of [legacy system] to AWS cloud, leveraging services such as S3, Redshift, and EMR, resulting in [Z%] cost savings and [A%] improvement in system performance
Previous Position
Job Title • Start Date • End Date
Company Name
  • Optimized [specific ETL process] using AWS Step Functions and Lambda, reducing runtime by [X%] and increasing data freshness for [business intelligence tool/dashboard]
  • Designed and implemented [type of data model] in Amazon Redshift, improving query performance by [Y%] and enabling real-time analytics for [specific business function]
Resume Skills
  • Data Warehousing & Architecture Design
  • [Preferred Programming Language(s), e.g., Python, Java, Scala]
  • [AWS Services, e.g., S3, Redshift, Lambda]
  • ETL Development & Data Pipeline Management
  • [Big Data Technology, e.g., Hadoop, Spark]
  • Database Management & SQL
  • [Data Modeling Tool, e.g., ER/Studio, ERwin]
  • Data Security & Compliance
  • [Data Integration Tool, e.g., Apache NiFi, Talend]
  • Performance Optimization & Monitoring
  • Collaboration & Stakeholder Communication
  • [Specialized Certification, e.g., AWS Certified Data Analytics - Specialty]
    • Certifications
    Official Certification Name
    Certification Provider • Start Date • End Date
    Official Certification Name
    Certification Provider • Start Date • End Date
    Education
    Official Degree Name
    University Name
    City, State • Start Date • End Date
    • Major: [Major Name]
    • Minor: [Minor Name]

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    Resume writing tips for AWS Data Engineers

    Many AWS Data Engineer resumes miss the mark by using vague titles or generic summaries. Hiring managers want clear alignment with the job description and measurable results that show your impact. Focus your resume on precise language and concrete achievements to stand out in 2025’s competitive market.
    • Avoid creative or broad job titles that confuse ATS and recruiters. Use the exact AWS Data Engineer title from the job posting to ensure your resume passes filters and immediately signals relevance to hiring managers.
    • Lead your professional summary with your years of experience and specific AWS services you’ve mastered. Quantify your impact with metrics that show how you improved data workflows or reduced costs rather than using generic statements.
    • Write bullet points that demonstrate ownership and business impact. Use strong action verbs like “architected” or “optimized” and include measurable outcomes, such as “reduced data processing time by 30% using AWS Glue workflows.”
    • Showcase your technical depth by linking skills like Python, SQL, and AWS services to real-world results. Highlight how your scalable ETL pipelines handle large data volumes while maintaining data quality and supporting business goals.

    Common Responsibilities Listed on AWS Data Engineer Resumes:

    • Design and implement scalable data pipelines using AWS Glue and Lambda.
    • Collaborate with data scientists to optimize machine learning models on AWS SageMaker.
    • Develop and manage data lakes on AWS S3 for efficient data storage.
    • Automate ETL processes using AWS Step Functions and Apache Airflow.
    • Ensure data security and compliance with AWS IAM and KMS policies.

    AWS Data Engineer resume headline examples:

    AWS Data Engineer job titles are all over the place, which makes your resume title even more important. You need one that matches exactly what you're targeting. Most AWS Data Engineer job descriptions use a clear, specific title. Mirror their language rather than getting creative. Headlines are optional but should highlight your specialty if used.

    Strong Headlines

    AWS Certified Data Engineer: Optimizing Big Data Pipelines

    Weak Headlines

    Experienced Data Engineer with AWS Knowledge

    Strong Headlines

    Innovative Cloud Architect Specializing in AWS Data Lakes

    Weak Headlines

    Hard-Working Professional Seeking AWS Data Role

    Strong Headlines

    Results-Driven AWS Data Engineer: 10x Performance Improvements

    Weak Headlines

    AWS Data Engineer with Strong Technical Skills
    🌟 Expert Tip
    "A lot of strong engineers get overlooked because they forget to tell their story clearly. A resume should guide the reader through your contributions and impact; don’t make them connect the dots themselves." - Wade Russ, Director of Data Engineering

    Resume Summaries for AWS Data Engineers

    AWS Data Engineer roles have become more performance-driven and results-focused than ever. Your resume summary serves as your strategic positioning statement, immediately communicating your value proposition to hiring managers who scan hundreds of applications. This critical section determines whether recruiters invest time reading your full resume. Most job descriptions require that an AWS Data Engineer has significant experience. Lead with your years of experience, highlight specific AWS services you've mastered, and quantify achievements with concrete metrics. Skip generic objectives unless you lack relevant experience. Align your summary directly with target job requirements.

    Strong Summaries

    • Innovative AWS Data Engineer with 7+ years of experience, specializing in big data analytics and machine learning pipelines. Reduced data processing time by 40% through optimized ETL processes using AWS Glue and Redshift. Expert in serverless architectures and real-time data streaming with Kinesis and Lambda.

    Weak Summaries

    • Experienced AWS Data Engineer with knowledge of various cloud technologies. Worked on multiple projects involving data processing and analytics. Familiar with AWS services and big data tools. Looking to contribute to a dynamic team and grow professionally.

    Strong Summaries

    • Results-driven AWS Data Engineer adept at designing scalable, cloud-native data solutions. Led migration of 10PB data warehouse to AWS, resulting in 30% cost savings. Proficient in AWS EMR, Athena, and QuickSight for advanced analytics and visualization. Passionate about implementing data governance best practices.

    Weak Summaries

    • Dedicated AWS Data Engineer seeking new opportunities. Proficient in database management and ETL processes. Worked on several data migration projects and implemented data warehouses. Eager to apply skills in a challenging environment.

    Strong Summaries

    • Forward-thinking AWS Data Engineer with expertise in building robust data lakes and warehouses. Architected a serverless data pipeline processing 1TB daily, leveraging AWS Step Functions and S3. Skilled in Python, Spark, and Terraform for infrastructure-as-code. Committed to driving data-driven decision-making.

    Weak Summaries

    • AWS Data Engineer with a strong background in cloud computing. Experienced in working with large datasets and creating data pipelines. Knowledgeable about AWS services and data analytics tools. Committed to delivering high-quality results and meeting project deadlines.

    Resume Bullet Examples for AWS Data Engineers

    Strong Bullets

    • Architected and implemented a serverless data pipeline using AWS Lambda and Step Functions, reducing data processing time by 60% and saving $50,000 annually in infrastructure costs

    Weak Bullets

    • Worked on AWS data engineering projects and helped maintain data pipelines

    Strong Bullets

    • Optimized Amazon Redshift cluster performance, resulting in a 40% improvement in query execution time and enabling real-time analytics for 5 million daily active users

    Weak Bullets

    • Used various AWS services to process and analyze large datasets for the company

    Strong Bullets

    • Led the migration of 10TB of on-premises data to AWS S3 and designed a data lake solution using AWS Glue and Athena, improving data accessibility by 80% for cross-functional teams

    Weak Bullets

    • Collaborated with team members to improve data quality and implement best practices

    Bullet Point Assistant

    Use the dropdowns to create the start of an effective bullet that you can edit after.

    The Result

    Select options above to build your bullet phrase...
    🌟 Expert tip
    "If you're early in your engineering career, use your resume to show you're resilient and adaptable—able to take on new problems and keep making progress, even when things get hard." - Wade Russ, Director of Data Engineering

    Essential skills for AWS Data Engineers

    Building scalable data pipelines on AWS requires mastering Python, SQL, and services like Glue, Redshift, and S3. Hiring managers prioritize candidates who demonstrate both technical depth and practical implementation experience. Can you articulate how your ETL processes handle millions of records while maintaining data quality? Your ability to connect complex architecture decisions to business outcomes will distinguish you from other AWS Data Engineer candidates.

    Hard Skills

    • AWS CloudFormation
    • AWS Lambda
    • AWS Glue
    • AWS Redshift
    • AWS EMR
    • SQL and NoSQL Databases
    • ETL (Extract, Transform, Load) Processes
    • Data Warehousing
    • Data Modeling
    • Data Pipeline Development
    • Python or Java Programming
    • Big Data Technologies (Hadoop, Spark, etc.)

    Soft Skills

    • Problem Solving and Critical Thinking
    • Attention to Detail and Accuracy
    • Collaboration and Cross-Functional Coordination
    • Communication and Presentation Skills
    • Adaptability and Flexibility
    • Time Management and Prioritization
    • Analytical and Logical Thinking
    • Creativity and Innovation
    • Active Learning and Continuous Improvement
    • Teamwork and Leadership
    • Decision Making and Strategic Planning
    • Technical Writing and Documentation

    Resume Action Verbs for AWS Data Engineers:

    • Designing
    • Developing
    • Implementing
    • Optimizing
    • Automating
    • Troubleshooting
    • Configuring
    • Deploying
    • Integrating
    • Scaling
    • Monitoring
    • Securing
    • Provisioning
    • Migrating
    • Customizing
    • Architecting
    • Streamlining
    • Validating

    Tailor Your AWS Data Engineer Resume to a Job Description:

    Highlight Your AWS Expertise

    Carefully examine the job description for specific AWS services and tools required, such as Redshift, S3, or Lambda. Ensure your resume prominently features your experience with these services in both your summary and work experience sections. If you have used similar cloud technologies, mention your transferable skills while being clear about your AWS-specific expertise.

    Showcase Data Pipeline and ETL Skills

    Identify the company's data processing needs and the role's focus on building and maintaining data pipelines. Tailor your work experience to highlight relevant ETL processes, data integration, and pipeline optimization projects. Use metrics to demonstrate the efficiency and scalability improvements you achieved in past roles.

    Emphasize Problem-Solving in Big Data Environments

    Look for any industry-specific challenges or big data requirements in the job posting. Adjust your experience to showcase your ability to solve complex data engineering problems, particularly in large-scale data environments. Highlight any experience with industry-specific data types or challenges, and demonstrate your capacity to innovate and optimize data solutions.

    ChatGPT Resume Prompts for AWS Data Engineers

    Writing an AWS Data Engineer resume with ChatGPT helps you keep up with evolving tools, complex cloud architectures, and growing expectations. These changes make it harder to capture your impact clearly. AI tools like Teal turn your real-world AWS projects and data pipelines into strong, focused resume content. Let’s get started with prompts.

    AWS Data Engineer Prompts for Resume Summaries

    1. Create a summary for me that highlights my experience designing scalable AWS data pipelines using services like Redshift, Glue, and Lambda.
    2. Write a resume summary emphasizing my expertise in optimizing ETL workflows and improving data reliability on AWS cloud platforms.
    3. Generate a concise summary showcasing my skills in automating data ingestion and transformation processes to support business intelligence goals.

    AWS Data Engineer Prompts for Resume Bullets

    1. Write achievement-focused bullet points describing how I reduced data processing time by X% using AWS Glue and optimized Spark jobs.
    2. Create measurable resume bullets detailing how I implemented secure data lakes on S3 that improved data accessibility and compliance.
    3. Generate bullets that explain how I automated monitoring and alerting for AWS data pipelines, resulting in Y% fewer downtime incidents.

    AWS Data Engineer Prompts for Resume Skills

    1. List key AWS data engineering skills I should include, focusing on tools like Redshift, Glue, Lambda, and CloudFormation.
    2. Help me structure a skills section that highlights my expertise in cloud data architecture, ETL automation, and data security on AWS.
    3. Suggest a skills list that balances technical AWS tools with soft skills like problem-solving and cross-team collaboration.

    Resume FAQs for AWS Data Engineers:

    How long should I make my AWS Data Engineer resume?

    Aim for a one-page resume if you have less than 10 years of experience, or two pages if you have more. This length ensures you highlight relevant skills and experiences without overwhelming recruiters. Focus on showcasing AWS-specific projects and achievements. Use bullet points for clarity and prioritize recent, impactful experiences. Tailor your resume to the job description, emphasizing skills like data pipeline development and AWS service proficiency.

    What is the best way to format my AWS Data Engineer resume?

    A hybrid resume format is ideal for AWS Data Engineers, combining chronological and functional elements. This format highlights technical skills and relevant experiences, crucial for showcasing AWS expertise. Key sections include a summary, skills, experience, projects, and certifications. Use clear headings and bullet points for readability. Emphasize AWS tools and technologies, and quantify achievements to demonstrate impact, such as optimizing data workflows or reducing costs.

    What certifications should I include on my AWS Data Engineer resume?

    Include certifications like AWS Certified Data Analytics – Specialty, AWS Certified Solutions Architect – Associate, and AWS Certified Big Data – Specialty. These certifications validate your expertise in AWS services and data engineering, making you a competitive candidate. Present certifications prominently in a dedicated section, listing the certification name, issuing organization, and date obtained. Highlighting these credentials demonstrates your commitment to staying current with industry standards.

    What are the most common mistakes to avoid on a AWS Data Engineer resume?

    Avoid common mistakes like overloading technical jargon, omitting quantifiable achievements, and neglecting soft skills. Ensure your resume is accessible to both technical and non-technical audiences by balancing technical details with clear, concise language. Highlight achievements with metrics, such as improving data processing efficiency by a percentage. Lastly, emphasize teamwork and communication skills, as collaboration is vital in data engineering roles. Always proofread for errors to maintain professionalism.

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