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Senior Data Engineer
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: TELECOMMUTE
From the employer’s posting- Employment
- Not stated
What you’ll work on
Full postingDesign, build and maintain scalable data pipelines deployed on AWS
Develop data processing solutions using Python, Pandas and PySpark
Build and manage workflows through AWS MWAA (Apache Airflow)
Develop and optimize AWS Athena queries for data analysis
Automate infrastructure provisioning and deployment with Terraform and GitHub Actions
From the employer’s posting
Design, build, and maintain scalable data pipelines deployed on AWS
Develop robust data processing solutions using Python, Pandas, and PySpark
Build and manage workflows through AWS Managed Workflows for Apache Airflow (MWAA)
Develop and optimize data queries using AWS Athena to support efficient data analysis
Automate infrastructure provisioning and deployment processes using Terraform and GitHub Actions
What you’ll bring
All qualificationsCore experience
- Minimum 3 years of relevant professional experience
- Strong core Python programming skills for data engineering
- Proficiency in PySpark for large-scale distributed data
- Experience with AWS MWAA or Apache Airflow
- Experience using Terraform for infrastructure automation
Qualification wording
A minimum of 3 years of relevant professional experience
Strong core Python programming skills for building data engineering solutions
Proficiency in PySpark for processing large-scale distributed data
Experience with AWS MWAA or Apache Airflow for orchestrating and managing data workflows
Experience using Terraform to automate infrastructure provisioning and management
Tools in this posting
- Python
- AWS
- Terraform
- Airflow
- pandas
- PySpark
Source — Tool mentions in context
We are looking for a hands-on Senior Data Engineer to build and support scalable data pipelines and cloud-based data solutions. The ideal candidate will have strong experience in Python, PySpark, AWS, Airflow, and DevOps, with a focus on production-quality engineering and automation Responsibilities Design, build, and maintain scalable data pipelines deployed on AWS Develop robust data processing solutions using Python, Pandas, and PySpark Build and manage workflows through AWS Managed Workflows for Apache Airflow (MWAA) Develop and optimize data queries using AWS Athena to support efficient data analysis Automate infrastructure provisioning and deployment processes using Terraform and GitHub Actions Implement testing, monitoring, troubleshooting, and data-quality controls to ensure pipeline reliability Participate in code reviews and help drive strong engineering best practices across the team Collaborate with engineering, product, and business teams within an Agile environment Requirements A minimum of 3 years of relevant professional experience Strong core Python programming skills for building data engineering solutions Hands-on experience with Python Pandas for data manipulation and analysis Proficiency in PySpark for processing large-scale distributed data Experience with AWS MWAA or Apache Airflow for orchestrating and managing data workflows Working knowledge of AWS Athena for querying and analyzing data stored in AWS Experience using Terraform to automate infrastructure provisioning and management Familiarity with GitHub Actions for building automated CI/CD workflows Solid understanding of DevOps practices applied to data engineering environments Strong data engineering, debugging, and problem-solving skills Upper-intermediate English language skills or higher (B1) Nice to have Experience using GitHub Copilot to support development, code reviews, testing, and release management Familiarity with CI/CD practices and tooling for streamlined software delivery Experience working within Agile/Scrum environments Background in financial services or investment management industries Experience with Gen AI application development and applying Gen AI within the software development lifecycle (SDLC)
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 4, 2026today
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EPAM Systems is hiring
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordYour next step
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Has EPAM Systems posted this job before?
Yes. We’ve followed 24 other Senior Data Engineer jobs there since November 2026. All 24 are still up.
Large employers often post one title for several teams, so these can be separate openings.
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
We are looking for a hands-on Senior Data Engineer to build and support scalable data pipelines and cloud-based data solutions. The ideal candidate will have strong experience in Python, PySpark, AWS, Airflow, and DevOps, with a focus on production-quality engineering and automation Responsibilities Design, build, and maintain scalable data pipelines deployed on AWS Develop robust data processing solutions using Python, Pandas, and PySpark Build and manage workflows through AWS Managed Workflows for Apache Airflow (MWAA) Develop and optimize data queries using AWS Athena to support efficient data analysis Automate infrastructure provisioning and deployment processes using Terraform and GitHub Actions Implement testing, monitoring, troubleshooting, and data-quality controls to ensure pipeline reliability Participate in code reviews and help drive strong engineering best practices across the team Collaborate with engineering, product, and business teams within an Agile environment Requirements A minimum of 3 years of relevant professional experience Strong core Python programming skills for building data engineering solutions Hands-on experience with Python Pandas for data manipulation and analysis Proficiency in PySpark for processing large-scale distributed data Experience with AWS MWAA or Apache Airflow for orchestrating and managing data workflows Working knowledge of AWS Athena for querying and analyzing data stored in AWS Experience using Terraform to automate infrastructure provisioning and management Familiarity with GitHub Actions for building automated CI/CD workflows Solid understanding of DevOps practices applied to data engineering environments Strong data engineering, debugging, and problem-solving skills Upper-intermediate English language skills or higher (B1) Nice to have Experience using GitHub Copilot to support development, code reviews, testing, and release management Familiarity with CI/CD practices and tooling for streamlined software delivery Experience working within Agile/Scrum environments Background in financial services or investment management industries Experience with Gen AI application development and applying Gen AI within the software development lifecycle (SDLC)
Source details
Source notes
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- Pay
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- Location & working pattern
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- Work authorization
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