Aws Data Engineer Jobs
Aws Data Engineer jobs are open across technology, finance, healthcare, and retail, from associate to staff and principal engineer levels, with specializations in pipeline architecture, cloud-native ETL, and lakehouse design. Find a role that fits from the openings below and apply directly.
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Job Title: AWS Certified Data Engineer
Job Description:
We are seeking a highly skilled and motivated AWS Certified Engineer to design, build, and optimize scalable data solutions within the Amazon Web Services (AWS) ecosystem. The ideal candidate will have strong expertise in big data processing using PySpark and a deep understanding of data warehousing concepts, including Hive and modern table formats like Iceberg. This role involves developing, deploying, and managing robust, efficient, and secure data pipelines and analytics solutions on AWS, leveraging core networking and compute services.
Responsibilities:
- AWS Solution Design & Implementation: Design, develop, and deploy scalable and cost-effective data solutions on AWS, leveraging services such as S3 (for data lakes), EC2, EMR, Glue, Athena, Lambda, Redshift, and Kinesis.
- Data Pipeline Development: Build and maintain robust ETL/ELT data pipelines using PySpark for data ingestion, transformation, and loading into various data stores, including those utilizing open table formats like Iceberg.
- Big Data Processing: Develop and optimize big data processing jobs using PySpark on AWS EMR or AWS Glue, handling large datasets efficiently and integrating with Iceberg table formats.
- Data Warehousing: Design, implement, and manage data warehousing solutions, including schema design, data modeling, and query optimization, with a focus on Hive and modern data lake table formats like Iceberg for historical data and analytical queries.
- Cloud Infrastructure & Networking: Implement secure and robust cloud infrastructure components, including VPCs, subnets, routing, and security groups, to ensure proper connectivity and isolation for data solutions.
- Containerized Workloads: Design, deploy, and manage containerized data processing applications on Amazon Elastic Kubernetes Service (EKS).
- Performance Tuning & Optimization: Optimize AWS resources and big data applications (Spark, Hive, Iceberg) for performance, cost, and efficiency.
- Data Governance & Security: Implement best practices for data security, access control, and compliance within AWS, including IAM policies, S3 bucket policies, and encryption.
- Monitoring & Troubleshooting: Set up monitoring, alerting, and logging for data pipelines and AWS infrastructure; troubleshoot and resolve issues promptly.
- Automation: Develop and maintain automation scripts using Python and shell scripting for infrastructure provisioning, deployment, and operational tasks.
- Collaboration: Work closely with data scientists, analysts, and other engineering teams to understand data requirements and deliver reliable data solutions.
Qualifications
- AWS Certification: Hold at least one AWS certification (e.g., AWS Certified Solutions Architect – Associate, AWS Certified Data Analytics – Specialty, AWS Certified Developer – Associate).
- AWS Services Expertise: Hands-on experience with key AWS services for data processing and storage including:
- Storage: S3 (for data lakes), EC2
- Data Processing: EMR, Glue, Athena, Lambda
- Networking: VPC, Subnets, Routing, Security Groups
- Containerization: EKS
- Big Data Processing: Strong proficiency in PySpark for developing complex data transformations and analytics.
- Data Lake Table Formats: Practical experience with Apache Iceberg for managing and querying data lakes.
- Data Warehousing: In-depth knowledge and practical experience with Apache Hive for data storage, querying, and schema management.
- Programming Languages:
- Python: Expert-level proficiency in Python for scripting, data manipulation, and AWS automation (Boto3).
- Shell Scripting: Proficient in shell scripting for automation and operational tasks.
- Database & SQL: Strong SQL skills for data querying and manipulation.
- Data Concepts: Solid understanding of ETL/ELT processes, data modeling, distributed computing, and data governance.
Good to Have Skills:
- Containerization Orchestration: Experience with Kubernetes for deploying and managing containerized applications.
- CI/CD: Experience with CI/CD tools and practices (e.g., AWS CodePipeline, GitHub Actions, GitLab CI) for automating deployment of data solutions.
- Orchestration: Experience with workflow orchestration tools like Apache Airflow.
- Version Control: Proficient in using Git for source code management.
- Other Big Data Technologies: Exposure to other big data technologies like Apache Kafka, Flink, or Presto.
Certifications:
- AWS Certified Solutions Architect – Associate/Professional
- AWS Certified Data Analytics – Specialty
- AWS Certified Developer – Associate
Salary Range: $125,000 to $140,000 per year
Location
Irving, TX
Job Function
TECHNOLOGY
Role
Engineer
Job Id
417220
Desired Skills
AWS
Salary Range
$125,000-$140,000 a year
Qualifications
BACHELOR OF COMPUTER SCIENCE
Job Title: AWS Certified Data Engineer
Job Description:
We are seeking a highly skilled and motivated AWS Certified Engineer to design, build, and optimize scalable data solutions within the Amazon Web Services (AWS) ecosystem. The ideal candidate will have strong expertise in big data processing using PySpark and a deep understanding of data warehousing concepts, including Hive and modern table formats like Iceberg. This role involves developing, deploying, and managing robust, efficient, and secure data pipelines and analytics solutions on AWS, leveraging core networking and compute services.
Responsibilities:
- AWS Solution Design & Implementation: Design, develop, and deploy scalable and cost-effective data solutions on AWS, leveraging services such as S3 (for data lakes), EC2, EMR, Glue, Athena, Lambda, Redshift, and Kinesis.
- Data Pipeline Development: Build and maintain robust ETL/ELT data pipelines using PySpark for data ingestion, transformation, and loading into various data stores, including those utilizing open table formats like Iceberg.
- Big Data Processing: Develop and optimize big data processing jobs using PySpark on AWS EMR or AWS Glue, handling large datasets efficiently and integrating with Iceberg table formats.
- Data Warehousing: Design, implement, and manage data warehousing solutions, including schema design, data modeling, and query optimization, with a focus on Hive and modern data lake table formats like Iceberg for historical data and analytical queries.
- Cloud Infrastructure & Networking: Implement secure and robust cloud infrastructure components, including VPCs, subnets, routing, and security groups, to ensure proper connectivity and isolation for data solutions.
- Containerized Workloads: Design, deploy, and manage containerized data processing applications on Amazon Elastic Kubernetes Service (EKS).
- Performance Tuning & Optimization: Optimize AWS resources and big data applications (Spark, Hive, Iceberg) for performance, cost, and efficiency.
- Data Governance & Security: Implement best practices for data security, access control, and compliance within AWS, including IAM policies, S3 bucket policies, and encryption.
- Monitoring & Troubleshooting: Set up monitoring, alerting, and logging for data pipelines and AWS infrastructure; troubleshoot and resolve issues promptly.
- Automation: Develop and maintain automation scripts using Python and shell scripting for infrastructure provisioning, deployment, and operational tasks.
- Collaboration: Work closely with data scientists, analysts, and other engineering teams to understand data requirements and deliver reliable data solutions.
Qualifications
- AWS Certification: Hold at least one AWS certification (e.g., AWS Certified Solutions Architect – Associate, AWS Certified Data Analytics – Specialty, AWS Certified Developer – Associate).
- AWS Services Expertise: Hands-on experience with key AWS services for data processing and storage including:
- Storage: S3 (for data lakes), EC2
- Data Processing: EMR, Glue, Athena, Lambda
- Networking: VPC, Subnets, Routing, Security Groups
- Containerization: EKS
- Big Data Processing: Strong proficiency in PySpark for developing complex data transformations and analytics.
- Data Lake Table Formats: Practical experience with Apache Iceberg for managing and querying data lakes.
- Data Warehousing: In-depth knowledge and practical experience with Apache Hive for data storage, querying, and schema management.
- Programming Languages:
- Python: Expert-level proficiency in Python for scripting, data manipulation, and AWS automation (Boto3).
- Shell Scripting: Proficient in shell scripting for automation and operational tasks.
- Database & SQL: Strong SQL skills for data querying and manipulation.
- Data Concepts: Solid understanding of ETL/ELT processes, data modeling, distributed computing, and data governance.
Good to Have Skills:
- Containerization Orchestration: Experience with Kubernetes for deploying and managing containerized applications.
- CI/CD: Experience with CI/CD tools and practices (e.g., AWS CodePipeline, GitHub Actions, GitLab CI) for automating deployment of data solutions.
- Orchestration: Experience with workflow orchestration tools like Apache Airflow.
- Version Control: Proficient in using Git for source code management.
- Other Big Data Technologies: Exposure to other big data technologies like Apache Kafka, Flink, or Presto.
Certifications:
- AWS Certified Solutions Architect – Associate/Professional
- AWS Certified Data Analytics – Specialty
- AWS Certified Developer – Associate
Salary Range: $125,000 to $140,000 per year
Location
Irving, TX
Job Function
TECHNOLOGY
Role
Engineer
Job Id
417220
Desired Skills
AWS
Salary Range
$125,000-$140,000 a year
Qualifications
BACHELOR OF COMPUTER SCIENCE
See All 46+ Aws Data Engineer Jobs
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Find Aws Data Engineer JobsAws Data Engineer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- Amazon Web Services (AWS)7
- Capgemini7
- NTT DATA3
- Tata Consultancy Services (TCS)3
- Cognizant2
Top Industries Hiring
- Technology & Software37
- Consulting & Professional Services6
- E-Commerce & Online Marketplaces5
- Retail5
- Distribution & Wholesale3
What Employers Look For
The qualifications that appear most often in aws data engineer jobs.
- 3 or more years of hands-on experience building and maintaining AWS data pipelines
- Proficiency in Python or Scala for data transformation and orchestration workflows
- Experience with AWS services including Glue, S3, Redshift, EMR, and Lambda
- Familiarity with SQL and query optimization on large distributed datasets
- AWS Certified Data Engineer or AWS Certified Solutions Architect certification preferred
- Bachelor's degree in computer science, data engineering, or a related technical field
Tips for Your Aws Data Engineer Job Search
Tailor your resume to pipeline patterns
Recruiters scan for specific AWS service combinations, so call out whether you build on Glue, EMR, Redshift, or Kinesis rather than listing cloud skills generically. A pipeline section showing input source, transform logic, and output target reads faster than a bullet list of tool names.
Apply early to roles that fit
Migrate Mate lists aws data engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter openings by stack depth required
Job postings that mention Terraform or CDK alongside Glue signal an infra-aware role, while postings focused on dbt and Redshift lean toward analytics engineering. Matching your stack depth to the posting prevents wasted interviews where expectations misalign early.
Quantify data volume in every project entry
Hiring managers at data-heavy companies compare candidates on scale handled, so replace vague descriptions with concrete figures like daily row counts, pipeline latency targets, or storage costs reduced. Avoid invented numbers by pulling actuals from CloudWatch logs or cost dashboards before you write.
Prep a live coding demo for the design round
Many aws data engineer panels include a whiteboard or shared-screen session where you sketch an ingestion architecture under constraints. Practice narrating trade-offs out loud, especially around partitioning strategy, failure handling, and cost versus latency, because interviewers evaluate reasoning as much as the final design.
Negotiate around total compute cost ownership
Offers sometimes include on-call rotations for production pipelines, which affects real compensation. Before accepting, ask how pipeline incidents are handled, whether there is a rotation schedule, and whether on-call is compensated separately so you can compare offers accurately.
Aws Data Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most aws data engineers?
The companies hiring the most aws data engineers right now include Amazon Web Services (AWS), Capgemini, and NTT DATA, with the largest share of openings in Georgia, California, and Texas, based on current listings on Migrate Mate as of June 2026. Demand is strongest at companies undergoing cloud migrations or building out real-time analytics infrastructure.
How many aws data engineer jobs are remote?
About 35% of aws data engineer openings are fully remote or hybrid as of June 2026, reflecting how portable cloud-native work tends to be. Roles focused on Redshift analytics engineering and dbt modeling show the highest remote share, while positions requiring hands-on infrastructure provisioning or on-prem integration are more likely to require on-site presence.
How do you become a aws data engineer?
Start by building a foundation in SQL, Python, and relational data modeling, then move into cloud-native tooling by completing AWS training and earning a foundational certification. Build portfolio projects that demonstrate an end-to-end pipeline using S3, Glue, and Redshift. From there, apply to entry-level data or analytics engineering roles to gain production experience before targeting mid-level aws data engineer positions.
Can you get an aws data engineer job with little experience?
Yes, but you need to offset limited work history with demonstrable project work. Build a public GitHub portfolio that shows a complete ingestion and transformation pipeline on AWS, even using free-tier resources. Target smaller companies or startups where generalist data engineers are valued over specialists, and highlight any adjacent experience in data analysis, backend development, or database administration that transfers directly.
What does the aws data engineer interview process look like?
The process typically starts with a recruiter screen focused on your AWS service experience and pipeline history, followed by a technical phone screen testing SQL and Python fundamentals. A system design round then asks you to architect a data pipeline under specific constraints. Final rounds often include a take-home or live coding exercise and a panel with engineering and analytics stakeholders who probe past project decisions and trade-offs.
Where can I find and apply to aws data engineer jobs?
You can find and apply to aws data engineer jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your stack and experience level, then apply directly to each one that fits.
See All 46+ Aws Data Engineer Jobs
Jump back to the full list of openings and apply to any aws data engineer role that fits.
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