Role Description We are building a next-generation AI Payments Engineering Team to design and deliver an AI-native Payments Operating System. This team will combine AI engineering, payments domain expertise, and production automation to transform high-effort, high-risk financial operations into scalable, intelligent, and auditable systems. The team will focus on automating critical Payments processes such as: - Least cost routing - Reconciliations - Audit - Incident automation The mission is to deliver efficiency with control integrity—not just automation speed. Key Responsibilities - Assess and map end-to-end Payments operations, including: - Reconciliations - Reporting & controls - Audit - Identify operational bottlenecks, capability gaps, and integration failures. - Define a Payments automation roadmap based on operational impact, regulatory risk, and scalability. - Design the AI-native Payments Operating System, including: - Agentic system design - Data pipelines - Integration with financial systems - Control and reporting frameworks - Design and implement an agentic Payments layer that: - Operates alongside the current stack - Automates workflows today - Enables gradual migration to a future-state architecture - Evaluate and simplify the existing Payments technology stack. - Represent Payments in the AI Center of Excellence, ensuring alignment across teams. - Classify all automation by risk tier before development. - Ensure each solution includes: - Named Process Owner - Documented data flows - Access controls - Full audit logging - Design automation that preserves control integrity in regulated environments. - Develop reusable frameworks, templates, and workflows. - Create documentation and standards to scale adoption. - Build dashboards and metrics to measure: - Efficiency gains - Cost reduction - Risk outcomes - ROI - Train Payments teams on AI-enabled workflows and tools. - Work closely with: - Payments Backend teams - Infrastructure & platform teams - Compliance & audit teams - AI Center of Excellence - Translate business and finance requirements into technical solutions and roadmaps. - Communicate effectively with technical and non-technical stakeholders. Qualifications - Hands-on experience deploying production-grade AI/agentic systems. - Strong understanding of: - LLM orchestration - Multi-agent systems - AI failure modes in high-stakes environments - Proficiency in: - Spring AI/Python-based orchestration - Workflow engines (n8n or equivalent) - AI APIs (Anthropic/Claude or similar) - Ability to deliver across the lifecycle: Discovery → Design → Build → Deploy → Optimize. - Strong architectural thinking for scalable, future-ready platforms. Ways of Working - Operates effectively in fast-paced, ambiguous environments. - Strong ownership mindset with a bias for execution. - Ability to bridge business and engineering domains.

• Integrate data sets from various source systems into unified formats • Perform ELT (Extract, Load, Transform) operations • Cleanse and transform data by applying business rules • Conduct data cleansing and validation • Monitor and troubleshoot data issues • Create and maintain conceptual, logical, and physical data models • Build and maintain data pipelines • Load processed data into analytical data stores • Monitor the performance and quality of data transfers • Integrate test suites into CI/CD pipelines • Optimize data flows for efficiency, scalability, and reliability • Collaborate with team members to gather requirements

• Design, implement, and continuously improve systems for data ingestion, processing, storage, and sharing within a Data Mesh platform across cloud and legacy DWH environments • Build and optimize AWS cloud and on-premises (Oracle DB) data architectures for performance, scalability, and reliability • Develop and maintain ETL / ELT pipelines using modern tools and frameworks to support regulatory, operational, and analytical needs across banking areas • Support and modernize legacy data systems, ensuring smooth migration toward cloud platform architecture • Uphold high standards of data quality, security, availability, and performance • Collaborate with analysts, software engineers, and business stakeholders to understand data needs and deliver solutions • Perform code reviews, troubleshoot software, and resolve defects • Implement monitoring and alerting for data workflows • Participate in the EOD process — including a one-week ROTA rotation with availability until 23:00–24:00 for incident troubleshooting and resolution • Work as part of a cross-functional Agile team in a Pan-Baltic international environment

• Define and evolve the group data platform architecture - evaluate and select the target Azure stack (Azure SQL, Synapse, Fabric) and own the migration roadmap to it. • Design the multi-source integration architecture: ingestion from multiple products with heterogeneous schemas, using an integration-layer pattern suited to many sources (medallion/lakehouse or Data Vault) beneath a dimensional serving layer. • Address multi-region and data residency requirements: regional hosting, cross-border transfer constraints, GDPR and local privacy law, tenant and country-level data isolation. • Collaborate with Infrastructure Architects on storage, compute, performance, and Azure cost optimization across regions. • Design and maintain the three modeling layers: raw/integration (multi-source harmonization), canonical/master data (shared entities such as clinic, patient, device, product across products and countries), and serving (Kimball fact/dimension models optimized for Power BI). • Define master data management approach: entity matching, survivorship, and golden-record rules across products. • Optimize analytical models for performance (incremental refresh, partitioning, query tuning). • Design, build, and maintain ETL/ELT pipelines consolidating data from product services into the platform. • Build and tune Power BI semantic models and support dashboard development. • Implement data quality, lineage, and observability - monitoring, alerting, reconciliation checks across sources. • Collaborate with Domain Architects and product teams to define data contracts and export interfaces from operational services. • Govern data ingestion - accuracy, performance, and alignment with security and privacy policies. • Establish and run group data governance: ownership, definitions (a shared business glossary across products), metadata management, documentation, and schema versioning. • Support customer data migration into Manage from legacy systems - mapping, transformation, and validation approaches. • Define reusable migration tooling and quality gates in collaboration with onboarding/delivery teams. • Work with business stakeholders across products and countries to translate analytical needs into platform capabilities and data models.