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How much do data engineer internship amazon jobs pay per hour?

As of Aug 25, 2026, the average hourly pay for data engineer internship amazon in Colorado is $26.73, according to ZipRecruiter salary data. Most workers in this role earn between $21.73 and $30.34 per hour, depending on experience, location, and employer.

What does a data engineer intern do at Amazon?

A Data Engineer Intern at Amazon works on designing, building, and maintaining scalable data pipelines and systems to support business analytics and decision-making. Interns typically collaborate with experienced data engineers and other team members to process large datasets, ensure data quality, and optimize performance. They may also help automate data collection, transformation, and storage processes, gaining hands-on experience with Amazon's cloud technologies and big data tools. The internship offers an opportunity to develop technical skills in databases, programming, and data modeling in a real-world, fast-paced environment.

What are the key skills and qualifications needed to thrive as a data engineer intern at Amazon?

To thrive as a Data Engineer Intern at Amazon, you need a solid understanding of data structures, algorithms, and proficiency in programming languages such as Python, Java, or Scala, often supported by progress towards a degree in computer science or a related field. Familiarity with SQL, cloud platforms (especially AWS), and big data tools like Hadoop or Spark is typically required. Strong problem-solving skills, eagerness to learn, and effective communication help interns collaborate and adapt in a fast-paced environment. These skills and qualities are crucial to efficiently manage data pipelines, contribute to impactful projects, and succeed within Amazon’s data-driven culture.

What types of projects and technologies do data engineer interns at Amazon typically work with?

As a Data Engineer Intern at Amazon, you can expect to work on projects involving large-scale data pipelines, data warehousing, and analytics solutions. Interns often gain hands-on experience with Amazon Web Services (AWS) tools such as Redshift, S3, and Glue, as well as programming languages like Python and SQL. You'll collaborate closely with software engineers, data scientists, and business analysts to design and optimize data systems that support Amazon's business operations. This role provides a strong foundation in both the technical and collaborative aspects of data engineering, offering ample learning opportunities in a fast-paced, innovative environment.

What is the difference between Data Engineer Internship Amazon vs Data Analyst Internship Amazon?

AspectData Engineer Internship AmazonData Analyst Internship Amazon
Required SkillsSQL, Python, ETL, data modelingSQL, Excel, data visualization tools
Work EnvironmentData pipelines, backend systems, cloud platformsData reporting, dashboards, business insights
Industry UsageTech, e-commerce, cloud servicesBusiness, marketing, finance

Both internships are common in Amazon's data teams but focus on different aspects. Data Engineer Internships involve building and maintaining data infrastructure, while Data Analyst Internships focus on analyzing data to generate insights. Candidates should choose based on their technical skills and career interests.

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Cities in Colorado with the most Data Engineer Internship Amazon job openings:

Data Engineer Internship Amazon jobs near you

AWS Data Engineer | Big Data & Cloud Data Engineer

Edgewater, CO

Long Finch Technologies
IT Services • 51 - 200 employees
Learn more about Long Finch Technologiesexternal

$117K - $140K/yr

Full-time

Posted 12 days ago

Job description

We are seeking an experienced AWS Data Engineer / Big Data Technology Lead to design, develop, and maintain scalable data solutions using AWS cloud technologies and big data frameworks. The ideal candidate will have strong experience building data pipelines, managing data platforms, and delivering enterprise-level analytics solutions.

Key Responsibilities
  • Design, develop, and maintain scalable data pipelines and architectures on AWS to support analytics, reporting, and operational workflows.
  • Develop ETL/ELT solutions using AWS services.
  • Build and manage cloud-based data lakes and data warehouse solutions using: Amazon S3, Amazon Athena, Amazon Redshift and Amazon RDS.
  • Design and implement scalable data architectures while ensuring data quality, security, and integrity.
  • Develop data processing workflows to support large-scale data ingestion and transformation.
  • Collaborate with data scientists, analysts, and business teams to curate and optimize production-ready datasets.
  • Monitor, troubleshoot, and improve data pipeline performance and reliability.
  • Work with DevOps tools and practices including Jenkins and Maven for deployment automation.
Required Experience & Skills
  • 7+ years of experience in Big Data Engineering, Data Engineering, or related roles.
  • Strong hands-on experience with AWS cloud services and Big Data technologies.
  • Experience designing, developing, and maintaining scalable data pipelines and cloud-based data architectures.
  • Proficiency in developing ETL/ELT pipelines using AWS services such as AWS Glue, Lambda, Kinesis, and Step Functions.
  • Experience building and managing data lakes and data warehouses using AWS services including S3, Athena, Redshift, and RDS.
  • Strong knowledge of data modeling, data integration, and ensuring data quality and integrity.
  • Experience working with Hadoop and other Big Data technologies.
  • Strong SQL skills with experience in databases such as Oracle 10g/11g/12c and SQL Server.
  • Experience with Unix/Linux environments and scripting.
  • Familiarity with DevOps tools such as Jenkins and Maven.
  • Experience with monitoring and logging tools such as Splunk is preferred.
  • Ability to collaborate with data scientists, analysts, and cross-functional teams to deliver production-ready data solutions.

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