Analytics Engineer [DWH]

• 2-3 years of experience in a similar role, such as Analytics Engineer, DWH Analyst, Data Analyst, or Data Engineer, with hands-on experience working with data warehouses
• Strong SQL skills, including complex queries, joins, aggregations, CTEs, window functions and data transformations
• Hands-on experience with data modeling, data transformations, and building or maintaining data marts
• Hands-on experience building and maintaining ETL/ELT transformations and data models using dbt or similar data transformation frameworks
• Good understanding of data quality principles and ability to troubleshoot data issues
• Ability to translate business requirements into structured data solutions
• Strong problem-solving skills and ability to work independently
• Strong communication skills and experience working with analysts, data engineers, and other stakeholders
• B1+ English for communication within an international team

• Experience in Banking or Fintech
• Experience with workflow orchestration tools such as Airflow
• Understanding of Data Vault or dimensional modeling approaches
• Experience working with large datasets

• Relocation support to one of our hubs - Cyprus, Serbia, Georgia or Kazakhstan - with assistance for the employee and their family
• Flexible work from one of our offices or remote
• Healthcare Coverage
• Education Budget: Language lessons, professional training and certifications
• Wellness Budget: Mental health and fitness activity reimbursements
• Vacation policy: 20 days of annual leave and paid sick leave

We are looking for an Analytics Engineer to join our DWH team.
Our bank runs on data-driven processes, and the DWH team plays a central role in enabling them. We build and maintain data pipelines from internal and external sources, develop data models and marts for reporting and analytics, and create tools that make working with data across the organization more reliable and scalable.
In this role, you will work at the intersection of analytics and data engineering, helping domain teams such as Product and Risk turn business requirements into reliable, production-grade data models and transformations. You will work across the data lifecycle - from analysing sources and designing data models to building, improving, and optimising ETL/ELT processes and data marts.

• Translating business and analytical requirements into scalable data models and ETL/ELT solutions
• Analyzing raw and processed data sources to identify the most suitable integration points
• Designing and developing logical data models and data marts for domain teams
• Building, maintaining, and optimizing data transformations using SQL and dbt
• Improving existing data flows and models for scalability, maintainability, and performance
• Investigating data quality issues and inconsistencies across sources, transformations, and data marts
• Collaborating with analysts and data engineers to integrate domain-specific data processes with core DWH components
• Documenting data models, lineage, integration points, and business rules to maintain a reliable DWH knowledge base