The client’s Digital organization is on a transformation journey to modernize data, analytics, and decision support across the bank. A key part of this journey is strengthening Data Warehouse and Data Mart capabilities to ensure that business-critical data for Finance, Risk, and Compliance is well-modeled, reliable, and usable.This assignment focuses on reinforcing and evolving the DW/DM layer, while integrating with upstream data ingestion and lake-based architectures on the AWS data platform.As a Data Engineer with focus on Data Warehousing and Data Modeling, you will play a central role in designing, developing, and maintaining curated data structures supporting analytical, regulatory, and business use cases in a banking context.You will collaborate closely with: Data Engineering Leads Other Data Engineers (platform, integration, lake-focused) Data Analysts and BI Developers Tech Leads and Product Owners Finance, Risk, and Compliance stakeholders Your work will bridge business context and technical implementation, ensuring data models are scalable, governed, and aligned with platform standards. Key Responsibilities Design, develop, and maintain Data Warehouse and Data Mart solutions on an AWS-based data platform Develop and evolve data models supporting financial, risk, and regulatory reporting Translate business requirements into robust analytical models Collaborate with integration teams to ensure reliable upstream data Build and maintain transformation pipelines using modern tooling Contribute to platform-level architecture and design decisions Apply DevOps principles to ensure reliability and maintainability Participate in solution design, code reviews, and continuous improvement Must-have experience / expertise: Data Warehousing and Data Modeling (DW/DM) Banking domain data, preferably Finance, Risk, or Compliance Strong SQL and ability to reason about complex data structures Experience with cloud-based data platforms (AWS or similar) Ability to connect business context with technical implementation Experience in cross-functional, agile teams Experience with a specific warehouse technology (e.g. Redshift) is valuable but not mandatory  Strongly preferred / added value: Data modeling methodologies (dimensional modeling, data marts) Infrastructure as Code (e.g. Terraform) Modern table formats (e.g. Apache Iceberg) Orchestration frameworks (e.g. Apache Airflow) Transformation tools (e.g. dbt) CI/CD pipelines (preferably GitHub Actions) Cloud access management and network infrastructure understanding Banking or financial services experience  Ways of working & soft skills: Structured, analytical, and quality-oriented mindset Self-driven and proactive, with strong ownership Collaborative and communicative across technical and business roles Ability to explain complex data concepts to non-technical stakeholders Excellent English communication skills (written and spoken)  Good to have: Experience with legacy DW technologies such as Oracle RDBMS or Informatica Familiarity with SAFe or other agile frameworks