Rishi Writes
AWS Data Engineer – (12 Years, Hybrid – Dallas, TX) Komm Force Solutions
Rishi Writes, Dallas, Texas, United States, 75215
About the Role – AWS Cloud Data Engineer
We are hiring a skilled
AWS Data Engineer
to help develop and optimize cloud-native data pipelines using
Python, AWS Glue, Redshift, and Kafka . This hybrid role in
Dallas, TX
offers the chance to work on mission-critical ETL workflows and data transformation solutions across large datasets. Key Responsibilities – Python-Based ETL & AWS Data Engineering
Build and automate
ETL pipelines using Python
for cloud data lakes and warehouses
Integrate with
AWS services
like S3, Glue, EMR, Redshift, Athena, Kinesis, and SageMaker
Design and execute
automated testing frameworks
for data validation and integrity
Create dashboards and reports to support
data visualization and insights
Collaborate with analysts, product managers, and developers to deliver accurate data solutions
Perform
data migration
from on-premises to AWS environments
Execute
DevOps and DataOps practices
for pipeline optimization
Troubleshoot data pipeline issues, investigate anomalies, and resolve performance bottlenecks
Required Skills – Python, AWS Glue, Redshift, and Kafka
Proficiency in
Python programming
for data automation
Hands-on experience with
AWS services : S3, Glue, Redshift, EMR, Athena, SageMaker
Experience with
streaming tools like Kafka
and structured data pipelines
Strong command of
SQL, Unix/Linux scripting , and CI/CD tools
Knowledge of
ETL technologies
such as Informatica, Ab Initio, Alteryx, or AWS Glue
Experience with
cloud data lake
and
on-prem to cloud migration
strategies
Familiarity with
testing and validating ETL workflows
Preferred Experience – Data Science and DevOps in Cloud
Exposure to
machine learning platforms
like SageMaker, H2O, or ML Studio
Knowledge of
Jenkins, GitLab , and CI/CD practices
Familiarity with
Agile and Waterfall methodologies
Experience with
test case management
and defect tracking tools
Hands-on testing experience with
S3, HDFS , and similar storage tools
Soft Skills – Collaboration and Ownership
Strong communication and ability to explain technical concepts clearly
Self-motivated and proactive with strong ownership mindset
Ability to guide junior developers or testers during data operations
Problem-solving and debugging skills across complex data environments
Flexible and adaptive in hybrid work setups
Ready to Apply?
If you are passionate about building high-impact data engineering solutions using Python and AWS, this is your chance to grow with a cloud-first team. Check out other positions
Let’s discuss your next career move 15 FAQs About This AWS Data Engineer Role
1. What is the primary focus of this AWS Data Engineer position?
To build, test, and manage automated cloud-based ETL pipelines using Python and AWS services. 2. Is this a remote position?
It’s a hybrid role requiring partial onsite presence in Dallas, TX. 3. How much experience is required?
Candidates with 6–10 years of data engineering and cloud infrastructure experience are preferred. 4. What tools are commonly used in this role?
Python, AWS Glue, Redshift, EMR, Kafka, Jenkins, GitLab, SQL, and Unix scripting. 5. Is experience in data science mandatory?
Not mandatory, but familiarity with platforms like SageMaker or H2O is a bonus. 6. Will I work on real-time data or batch pipelines?
Both – the role involves streaming platforms like Kafka and batch processing using AWS tools. 7. Is prior DevOps experience necessary?
DevOps and DataOps exposure is highly preferred to support CI/CD for data pipelines. 8. Will I work directly with business stakeholders?
Yes, you’ll collaborate closely with analysts, developers, and product teams. 9. Is on-prem to cloud migration experience needed?
Yes, data migration experience is a plus. 10. Will I need to design dashboards or just backend pipelines?
You’ll support report creation and help design dashboards for data visualization. 11. What kind of scripting is expected?
Unix/Linux shell scripting and Python automation for testing and deployment. 12. What’s the interview process like?
Initial screening, followed by a technical interview and data engineering assessment. 13. Do I need experience with specific testing tools?
Experience with test case management and defect tracking tools is preferred. 14. What are typical KPIs or success metrics in this role?
Uptime, accuracy, data freshness, test coverage, and pipeline performance. 15. Can I expect growth opportunities?
Yes, you’ll be working on cutting-edge cloud solutions with potential for leadership.
#J-18808-Ljbffr
We are hiring a skilled
AWS Data Engineer
to help develop and optimize cloud-native data pipelines using
Python, AWS Glue, Redshift, and Kafka . This hybrid role in
Dallas, TX
offers the chance to work on mission-critical ETL workflows and data transformation solutions across large datasets. Key Responsibilities – Python-Based ETL & AWS Data Engineering
Build and automate
ETL pipelines using Python
for cloud data lakes and warehouses
Integrate with
AWS services
like S3, Glue, EMR, Redshift, Athena, Kinesis, and SageMaker
Design and execute
automated testing frameworks
for data validation and integrity
Create dashboards and reports to support
data visualization and insights
Collaborate with analysts, product managers, and developers to deliver accurate data solutions
Perform
data migration
from on-premises to AWS environments
Execute
DevOps and DataOps practices
for pipeline optimization
Troubleshoot data pipeline issues, investigate anomalies, and resolve performance bottlenecks
Required Skills – Python, AWS Glue, Redshift, and Kafka
Proficiency in
Python programming
for data automation
Hands-on experience with
AWS services : S3, Glue, Redshift, EMR, Athena, SageMaker
Experience with
streaming tools like Kafka
and structured data pipelines
Strong command of
SQL, Unix/Linux scripting , and CI/CD tools
Knowledge of
ETL technologies
such as Informatica, Ab Initio, Alteryx, or AWS Glue
Experience with
cloud data lake
and
on-prem to cloud migration
strategies
Familiarity with
testing and validating ETL workflows
Preferred Experience – Data Science and DevOps in Cloud
Exposure to
machine learning platforms
like SageMaker, H2O, or ML Studio
Knowledge of
Jenkins, GitLab , and CI/CD practices
Familiarity with
Agile and Waterfall methodologies
Experience with
test case management
and defect tracking tools
Hands-on testing experience with
S3, HDFS , and similar storage tools
Soft Skills – Collaboration and Ownership
Strong communication and ability to explain technical concepts clearly
Self-motivated and proactive with strong ownership mindset
Ability to guide junior developers or testers during data operations
Problem-solving and debugging skills across complex data environments
Flexible and adaptive in hybrid work setups
Ready to Apply?
If you are passionate about building high-impact data engineering solutions using Python and AWS, this is your chance to grow with a cloud-first team. Check out other positions
Let’s discuss your next career move 15 FAQs About This AWS Data Engineer Role
1. What is the primary focus of this AWS Data Engineer position?
To build, test, and manage automated cloud-based ETL pipelines using Python and AWS services. 2. Is this a remote position?
It’s a hybrid role requiring partial onsite presence in Dallas, TX. 3. How much experience is required?
Candidates with 6–10 years of data engineering and cloud infrastructure experience are preferred. 4. What tools are commonly used in this role?
Python, AWS Glue, Redshift, EMR, Kafka, Jenkins, GitLab, SQL, and Unix scripting. 5. Is experience in data science mandatory?
Not mandatory, but familiarity with platforms like SageMaker or H2O is a bonus. 6. Will I work on real-time data or batch pipelines?
Both – the role involves streaming platforms like Kafka and batch processing using AWS tools. 7. Is prior DevOps experience necessary?
DevOps and DataOps exposure is highly preferred to support CI/CD for data pipelines. 8. Will I work directly with business stakeholders?
Yes, you’ll collaborate closely with analysts, developers, and product teams. 9. Is on-prem to cloud migration experience needed?
Yes, data migration experience is a plus. 10. Will I need to design dashboards or just backend pipelines?
You’ll support report creation and help design dashboards for data visualization. 11. What kind of scripting is expected?
Unix/Linux shell scripting and Python automation for testing and deployment. 12. What’s the interview process like?
Initial screening, followed by a technical interview and data engineering assessment. 13. Do I need experience with specific testing tools?
Experience with test case management and defect tracking tools is preferred. 14. What are typical KPIs or success metrics in this role?
Uptime, accuracy, data freshness, test coverage, and pipeline performance. 15. Can I expect growth opportunities?
Yes, you’ll be working on cutting-edge cloud solutions with potential for leadership.
#J-18808-Ljbffr