Senior Data Engineer (Ukraine)

Overview:

We are looking for a Data Engineer to design, build, and operate production data pipelines and platforms that support large scale AI and ML workloads. The role focuses on end to end data lifecycle management, AWS based infrastructure, and collaboration with ML and data teams.

Minimum Qualifications:

• Bachelor’s or master’s degree in computer science, data engineering, software engineering, or related field
• 2-3+ years of experience building production data pipelines and data platforms for AI or ML systems
• Strong proficiency in Python, C++ and distributed data processing frameworks
• Hands on experience with AWS services including S3, EC2, SageMaker, and Glue
• Experience designing data systems that support large scale ML training and experimentation
• Knowledge of data governance, access control, and lifecycle management
• Experience working with ML, data science, operations, and cloud engineering teams.

Preferred Qualifications:

• Experience building pipelines across edge devices and cloud systems
• Background working with large scale sensor, image, or IoT data
• Familiarity with data labeling tools and annotation workflows
• Experience with dataset versioning, lineage, and reproducibility systems
• Understanding of privacy, compliance, or regulated data environments
• Experience supporting global multi region data platforms.

Key Responsibilities:

End to End Data Pipeline Ownership
- Design, build, and maintain research and production data pipelines spanning edge devices, cloud services, and centralized platforms
- Own the full data lifecycle including collection, ingestion, processing, obfuscation, versioning, access, retention, and retirement

Edge to Cloud Data Flow
- Develop resilient ingestion pipelines that handle device variability and connectivity challenges
- Support secure data transfer from field environments to cloud storage
- Collaborate with operations teams to improve data coverage, observability, and reliability

Data Quality, Governance, and Compliance
- Implement privacy preserving transformations and obfuscation pipelines
- Build automated data cleaning and validation processes
- Establish data lineage, retention policies, and access controls to ensure compliance and traceability

Data Services for AI and ML
- Provide scalable data services for training, evaluation, and research experimentation
- Support continuous data refresh and retraining workflows
- Integrate with labeling and annotation systems
- Enable efficient access patterns for large scale ML workloads

AWS Based Cloud Infrastructure
- Build and optimize pipelines using AWS services such as S3, EC2, SageMaker, Lambda, Glue, and Step Functions
- Design for cost efficiency, performance, and reliability at scale

Collaboration and Feedback Loops
- Work with AI and ML engineers, scientists, and data teams to gather data requirements
- Translate feedback into automated improvements in data collection and labeling
- Support teams with exploratory analysis and data issue debugging

Scaling the Data Factory
- Design and maintain data schemas, dataset versioning, and data factory updates
- Architect global scale data systems across large device fleets
- Ensure the platform is flexible for research and reliable for production.

What you will love about working here?

• We care about all our employees and want them to feel as comfortable as possible. That's why we offer them health insurance from the first days, regardless of the probationary period.
• The gift from the company - Christmas holidays from 25 December to 31 December.
• Сooperation with Superhumans center and Veteran HUB. Capgemini Engineering has supported the launch of psychological rehabilitation department of Superhumans. Our team also donnated over UAH 500 000 prosthetics for three Ukrainian defenders. Currently, we support psychological counseling provided by the Veteran Hub, and we have implemented a internal policy making the company friendly to military and veterans with the assistance of the Hub.