Design, build, and optimize data ingestion, transformation, and processing pipelines using Apache Spark–based platforms
Implement and maintain scalable solutions aligned with Data Lakehouse architecture
Use SQL and Python to develop robust data workflows
Understand data models and implement them as solutions
Develop and manage YAML-based configurations for pipeline metadata
Ensure data quality, validation, security, and performance across data assets
Collaborate closely with various teams to understand requirements
Support orchestration workflows on AWS MWAA or similar platforms
Monitor, troubleshoot, and optimize data pipelines in production environments

3–5 years of experience as a Data Engineer or similar role
Strong experience with AWS data stack: S3, EMR, Glue data catalog, Kafka, Athena, IAM and orchestration tools
Strong programming experience with Python
Expertise in SQL and performance optimization
Strong understanding of Data Lakehouse, ETL/ELT architectures, and distributed data processing
Practical experience in data modeling and industry-standard techniques
Proficiency with YAML for configuration and pipeline metadata management
Fluent English is required in spoken and written communication

Health insurance after the first 3 months in all three Baltic states
Health days in case of sickness without a doctor's note needed
Additional weeks of vacation for employees after 1 year
Additional days off for volunteering purposes
30 fully paid calendar days for military training every year
Internal and external training programs, LinkedIn learning, workshops, conferences, online training
Special offers & pricing for Luminor products & services
Flexible working hours
Hybrid work and the capability to work from anywhere in the EU, Iceland, Switzerland, and the UK (90 days per year)

Senior Data Engineer – Payments Unit

Key skills