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Senior Data Software 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 postingPrepare machine learning datasets and manage scoring processes
Partner with Data Scientists to build and roll out key ML models
Expand ML predictions delivery via API-based scoring and data warehouse access
Embed automated workflows into ML data engineering for monitoring and alerting
Architect and build scalable, reusable data models
From the employer’s posting
Support and take part in preparing machine learning datasets and managing scoring processes
Partner with Data Scientists to build and roll out key machine learning models
expanding how ML predictions and recommendations are distributed through API-based scoring and data warehouse access
Embed automated workflows into ML Data Engineering processes to support monitoring and alerting
Architect and build scalable, reusable data models
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 1, 20262 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Oct 6, 2026
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 recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- Azure
- Databricks
- Docker
- GCP
- Kubernetes
- Snowflake
- FastAPI
- NumPy
- pandas
- Go
Where the posting names them
We are looking for a Senior Data Software Engineer to join our team. This role sits at the heart of how data-driven insights are built, delivered, and scaled across the organization. You'll partner with skilled professionals across multiple disciplines to bring innovative solutions to complex challenges. It's a great opportunity for someone who enjoys combining strong engineering practices with a passion for data-driven decision making. Responsibilities Support and take part in preparing machine learning datasets and managing scoring processes Build and maintain deep familiarity with real-time and snapshot-based data sources company-wide, along with the Analytics team members who own them Partner with Data Scientists to build and roll out key machine learning models Advance the ML component of the Enterprise Data Platform strategy, expanding how ML predictions and recommendations are distributed through API-based scoring and data warehouse access Embed automated workflows into ML Data Engineering processes to support monitoring and alerting Apply performance tuning techniques and optimize query execution Serve as a go-to resource for the Machine Learning team on matters related to Data Lifecycle Management Stay current with evolving technologies across databases, data lakes, and data warehouse platforms Work alongside Analytics team members to follow documented architecture, design, and deployment standards, ensuring policy compliance Architect and build scalable, reusable data models Requirements Bachelor's or Master's degree in Computer Science, Computer Engineering, Mathematics, Statistics, or a related field 3+ years of hands-on, relevant professional experience Background in Machine Learning, Data Engineering, ML Infrastructure, or similar areas, with a solid foundation in probability, statistics, and machine learning concepts Hands-on experience with Databricks, including Unity Catalog, along with cloud ML platforms such as Azure ML Studio, AWS, or GCP Experience building and scaling end-to-end ML systems, ETL/ELT pipelines, and data workflows using Python, NumPy, Pandas, and SQL Experience developing scalable APIs and microservices with FastAPI, alongside Snowflake or comparable data warehouse technologies Strong communication and cross-functional collaboration skills, with working English fluency at B2 level or higher, supporting clear understanding of business requirements Nice to have Experience deploying production workloads using Docker and Kubernetes, with a solid grasp of scalable, reliable software engineering practices Familiarity with Microsoft Azure for building and managing cloud-based solutions
Your next step
Complete your application on careers.epam.com. The employer’s form will show what is required.
Already applied? Track this application
Has EPAM Systems posted this job before?
Yes. We’ve followed 24 other Senior Data Software 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.
Job description
We are looking for a Senior Data Software Engineer to join our team. This role sits at the heart of how data-driven insights are built, delivered, and scaled across the organization. You'll partner with skilled professionals across multiple disciplines to bring innovative solutions to complex challenges. It's a great opportunity for someone who enjoys combining strong engineering practices with a passion for data-driven decision making. Responsibilities Support and take part in preparing machine learning datasets and managing scoring processes Build and maintain deep familiarity with real-time and snapshot-based data sources company-wide, along with the Analytics team members who own them Partner with Data Scientists to build and roll out key machine learning models Advance the ML component of the Enterprise Data Platform strategy, expanding how ML predictions and recommendations are distributed through API-based scoring and data warehouse access Embed automated workflows into ML Data Engineering processes to support monitoring and alerting Apply performance tuning techniques and optimize query execution Serve as a go-to resource for the Machine Learning team on matters related to Data Lifecycle Management Stay current with evolving technologies across databases, data lakes, and data warehouse platforms Work alongside Analytics team members to follow documented architecture, design, and deployment standards, ensuring policy compliance Architect and build scalable, reusable data models Requirements Bachelor's or Master's degree in Computer Science, Computer Engineering, Mathematics, Statistics, or a related field 3+ years of hands-on, relevant professional experience Background in Machine Learning, Data Engineering, ML Infrastructure, or similar areas, with a solid foundation in probability, statistics, and machine learning concepts Hands-on experience with Databricks, including Unity Catalog, along with cloud ML platforms such as Azure ML Studio, AWS, or GCP Experience building and scaling end-to-end ML systems, ETL/ELT pipelines, and data workflows using Python, NumPy, Pandas, and SQL Experience developing scalable APIs and microservices with FastAPI, alongside Snowflake or comparable data warehouse technologies Strong communication and cross-functional collaboration skills, with working English fluency at B2 level or higher, supporting clear understanding of business requirements Nice to have Experience deploying production workloads using Docker and Kubernetes, with a solid grasp of scalable, reliable software engineering practices Familiarity with Microsoft Azure for building and managing cloud-based solutions
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