Our client is a stable, privately owned IT consulting company with over 20 years of experience delivering enterprise-scale technology projects across multiple industries. Their long-standing partnerships with large organizations reflect both technical credibility and financial stability, supported by consistently strong external credit ratings. The company operates with a lean core team, low turnover, and a collaborative, trust-based culture focused on long-term cooperation and high-quality delivery. They work across diverse domains including telecommunications, healthcare, and academia, offering engineers the opportunity to contribute to varied, complex initiatives while maintaining a flexible and supportive work environment.

We are looking for a Senior Data Engineer to work on large-scale data environments involving high-volume datasets, complex integrations, and analytical use cases. This is a hands-on engineering role focused on building and maintaining data pipelines, understanding end-to-end data flows, and troubleshooting data quality issues across distributed systems.

You will work closely with engineers, analysts, and stakeholders to ensure reliable, scalable, and well-structured data solutions that support analytics and machine learning initiatives.

Tasks:

Design, build, and maintain scalable data ingestion and ETL/ELT pipelines
Work with large datasets and interfaces feeding enterprise-scale databases
Analyze data flows and troubleshoot inconsistencies across systems
Perform root cause analysis to trace incorrect outputs back to source systems
Prepare datasets for analytical and machine learning use cases
Contribute to data modeling and data mapping activities
Support ML initiatives through data preparation, evaluation, and optimization
Define and implement data quality monitoring frameworks
Develop visualizations to support data interpretation and insights
Solid experience in data engineering or closely related backend/data roles
Strong hands-on experience with Python
Experience with Scala or Java is a strong advantage
Proven background working with large datasets and distributed systems
Experience with ETL/ELT pipelines and data integration solutions
Familiarity with Kafka or other streaming technologies is a plus
Understanding of data modeling, data mapping, and data lineage
Knowledge of statistics and applied mathematics
Exposure to machine learning workflows and model lifecycle concepts
Strong analytical mindset and structured troubleshooting approach
Systems thinking and ability to work independently
Long-term, stable projects
Flexible hybrid working model
Fully flexible working hours
Diverse projects and technology exposure
Supportive team culture
Training and professional development support
Flat structure and collaborative environment