Senior Data Engineer

• Bachelor's or Master's degree in a relevant field
• 5+ years in data engineering or SQL heavy backend roles
• Proven experience working with structured enterprise data models
• Experience in finance or banking is a strong advantage
• Expert level SQL (complex queries, tuning, optimization)
• Experience working with and extending established data models (Data Vault, dimensional, SCD, 3NF)
• Strong experience developing scalable SQL driven transformations
• Commitment to building reliable, maintainable, and well tested data solutions
• Strong focus on quality, monitoring, reliability, and automation
• Ownership mentality across the data lifecycle
• Ability to refine requirements and collaborate with technical and business teams
• Creative, proactive, and improvement oriented approach
• Strong communication and stakeholder engagement abilities
• Analytical, detail oriented, and proactive problem solver
• Comfortable in agile, iterative delivery environments
• English: fluent (written and spoken)

Python knowledge and BI tool experience (e.g., Tableau, Power BI) are valuable pluses that support automation, data preparation, and efficient analytics delivery.

• Python is a strong plus for automation, API integrations, and workflow enhancement
• Experience with Tableau, Power BI, or other BI tools is a strong plus
• Experience working in banking, financial services, or regulated environments is a strong advantage
• German and/or French: nice-to-have
• Experience with Temenos T24/Transact, Temenos Triple'A, or similar core banking platforms

• Private health insurance
• EPAM Employees Stock Purchase Plan
• 100% paid sick leave
• Referral Program
• Professional certification
• Language courses

A Senior Data Engineer brings deep expertise in SQL development, data modeling, and stakeholder collaboration to deliver high-quality, reliable, and business-critical datasets. The role centers on building and optimizing SQL-driven data transformations, supporting analytical, operational, and regulatory reporting needs. Experience in banking or financial environments is a strong advantage.

• Develop and optimize complex SQL transformations supporting analytics, reporting, and financial/regulatory needs
• Work with and contribute to existing enterprise data models (Data Vault, dimensional, 3NF, domain driven structures)
• Ensure SQL logic is performant, maintainable, and aligned with modelling standards
• Implement and maintain data quality checks, validation rules, and SQL testing
• Maintain clear documentation, lineage, and metadata to support transparency and governance
• Support reduction of architectural debt and help maintain a clean, consistent data environment
• Build and maintain SQL centric ETL/ELT data pipelines
• Use Python where beneficial for automation, API integration, or workflow efficiency
• Implement ingestion patterns for batch, incremental, and near real time data flows
• Ensure monitoring, observability, and reliable operation of data workflows
• Partner with finance, risk, compliance, and business teams to translate requirements into high quality data solutions
• Work closely with data architects to ensure alignment with modelling standards
• Support troubleshooting across ingestion, modelling, and operational processes
• Contribute to platform evolution, engineering improvements, and roadmap initiatives
• Communicate effectively with both technical and business stakeholders
• Collaborate with platform and infrastructure teams to operate SQL workloads reliably in production
• Apply engineering best practices: version control, documentation, testing, code quality