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Data Engineer Internship Amazon Jobs in Tennessee

Amazon

Operations Reliability Engineer

Nashville, TN

$99K - $124K/yr

Amazon is seeking an Operations Reliability Engineer to support the Amazon Logistics North America ... We are looking for an individual who has experience creating scalable, data-driven solutions to ...

Amazon

Operations Reliability Engineer

Nashville, TN · On-site

$99K - $124K/yr

Amazon is seeking an Operations Reliability Engineer to support the Amazon Logistics North America ... We are looking for an individual who has experience creating scalable, data-driven solutions to ...

Amazon

Operations Reliability Engineer

Nashville, TN · On-site

$99K - $124K/yr

Amazon is seeking an Operations Reliability Engineer to support the Amazon Logistics North America ... We are looking for an individual who has experience creating scalable, data-driven solutions to ...

Deloitte

Lead Forward Deployed Engineer - AWS

Nashville, TN · On-site

$99K - $130K/yr

... AI&Data including hands on experience with one of the following key platforms/products: Amazon ... Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science ...

Deloitte

Senior Forward Deployed Engineer- AWS

Nashville, TN · On-site

$100K - $138K/yr

... AI&Data including hands on experience with one of the following key platforms/products: Amazon ... Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science ...

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Data Engineer Internship Amazon information

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 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.

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.
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Data Engineer Internship Amazon jobs near you

Cloud Engineer- Data/AI Focused

Innovative Solutions

Nashville, TN

$100K - $160K/yr

Full-time

Re-posted yesterday

Job description

As a Data & AI Engineer, you’ll build and deploy modern data pipelines and generative AI solutions on AWS — from scalable ETL/ELT workflows and cloud data platforms to production-grade RAG systems and AI agents. One engagement you may be designing and optimizing data pipelines using Glue, Lambda, and Redshift, the next you’re building GenAI applications powered by Bedrock, Claude, and vector databases to enable intelligent search and automation. You’ll work directly with clients to deliver end-to-end data and AI solutions that are secure, scalable, and production-ready.

What You’ll Do:

  • Implementing data pipelines using AWS services such as Glue, Lambda, Step Functions, and EMR
  • Creating and maintaining data extraction, transformation, and loading processes
  • Configuring and optimizing AWS database services including RDS, Aurora, Redshift, and DynamoDB
  • Implementing data lakes using S3 and related AWS services
  • Designing and building production-ready Generative AI applications using Amazon Bedrock and foundation models such as Anthropic Claude
  • Building and optimizing RAG (Retrieval-Augmented Generation) pipelines with vector databases
  • Developing AI agents and multi-agent orchestration systems using frameworks like LangChain or LlamaIndex
  • Writing and testing SQL queries and stored procedures
  • Documenting technical solutions and providing knowledge transfer to customers
  • Supporting the implementation of data governance and security controls
  • Troubleshooting and resolving issues with data pipelines and AI services
  • Participating in code reviews and implementing feedback
  • Assisting with proof-of-concept implementations for customer engagements

Required Skills:

  • 5+ years of software engineering experience with at least 2+ years focused on AI/ML, data engineering, or cloud-native development
  • 2+ years of hands-on AWS experience with production deployments
  • 1+ years of direct Generative AI experience (LLMs, embeddings, RAG, agents)
  • Proven track record delivering production AI applications from concept to deployment
  • Strong understanding of software engineering best practices (version control, testing, code review, documentation)
  • Experience working in agile/scrum environments with distributed teams
  • Excellent problem-solving skills and ability to work independently with minimal supervision
  • Strong written and verbal communication skills for client-facing interactions

Preferred:

  • Technical familiarity with AWS data and AI/ML services and modern data engineering practices
  • Hands-on experience with ETL/ELT processes and data transformation
  • Exposure to Generative AI concepts including LLMs, embeddings, RAG, and agent frameworks
  • Ability to write and optimize SQL queries across various database platforms
  • Knowledge of data modeling concepts and best practices
  • Strong analytical and problem-solving skills
  • Eagerness to learn new technologies and keep up with cloud and AI innovations
  • Excellent communication skills with the ability to explain technical concepts clearly
  • Attention to detail and commitment to solution quality
  • Collaborative mindset with strong teamwork capabilities
  • Experience or interest in automation and infrastructure as code
The salary range provided is a general guideline. When extending an offer, Innovative considers factors including, but not limited to, the responsibilities of the specific role, market conditions, geographic location, as well as the candidate’s professional experience, key skills, and education/training.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.