• Verantwortung für die Weiterentwicklung und Optimierung von Data Migration Services • Unterstützung bei der Anbindung neuer Kunden an moderne Steuerlösungen • Gestaltung, Optimierung und Skalierung von Migrationsstrecken sowie Analyse komplexer Datenstrukturen • Entwicklung intelligenter Mapping-Logiken und automatisierter Cleansing-Strategien • Entwicklung automatisierter Tests zur Sicherstellung der Datenintegrität und Datenqualität über den gesamten Migrationsprozess hinweg • Aufbereitung von Migrations- und Testdaten, Überwachung der Verarbeitungsprozesse und Fehleranalyse • Aktive Mitwirkung in der kontinuierlichen Weiterentwicklung von Entwicklungsprozessen, Tools und Best Practices

CAPCO POLAND Capco Poland is a global technology and management consultancy specializing in driving digital transformation across the financial services industry. We are passionate about helping our clients succeed in an ever-changing industry. We are also: - Experts in banking and payments, capital markets and wealth and asset management - Committed to growing our business and hiring the best talent to help us get there - Focused on maintaining our nimble, agile and entrepreneurial culture About the role: We are looking for a Senior Data Solution Engineer to join our data engineering team and help turn complex data into actionable, business-ready insights. In this role, you will design and deliver scalable data solutions, including domain data marts, semantic models, and Power BI dashboards. You will act as the bridge between business stakeholders and engineering teams, ensuring that data platforms are robust, performant, and aligned with business needs. You will also contribute to building and evolving a modern data platform, including migrating existing analytics solutions to the cloud and continuously improving the ecosystem. Key Responsibilities: - Architect and deliver business data marts in Snowflake, leading the development of production-grade dbt models - Establish and maintain standards for data modeling, documentation, and testing - Collaborate with the Data Architect to evolve and enhance the Data Vault 2.0 architecture - Lead the design and implementation of the semantic layer in Power BI, including development of reports and dashboards - Optimize performance and cost efficiency across data models, queries, and BI solutions - Collaborate with cross-functional teams (data engineers, business analysts, delivery leads, and stakeholders) to design and deliver data solutions - Embed SLAs, operational processes, and monitoring capabilities into solutions - Participate in incident management, root cause analysis, and continuous improvement activities - Mentor and coach team members, fostering knowledge sharing and best practices - Drive and enhance CI/CD pipelines (Azure DevOps), including automated testing, deployments, and rollback strategies - Implement and enforce data security and governance frameworks, including Snowflake roles/policies and Power BI Row-Level Security (RLS) Required Skills & Experience: - Proven experience in data solution engineering or analytics engineering within enterprise environments - Strong hands-on expertise in: - Solid experience implementing Data Vault 2.0, data marts, and semantic models - Experience in financial services or similar regulated industries - Good understanding of CI/CD practices, ideally using Azure DevOps - Familiarity with Azure Data Factory and/or Python is a plus - Experience with cloud migration, particularly from on-premise data warehouses - Comfortable working in Agile/Scrum environments - Fluent in English; German is an advantage ONLINE RECRUITMENT PROCESS STEPS - Screening call with the Recruiter - Competencies interview with Capco Hiring Manager - Client’s interview - Feedback / Offer We offer a flexible collaboration model based on a B2B contract, with the opportunity to work on diverse projects.

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth. • Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies. • Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices. • Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions). • Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques. • Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability. • Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards. • Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making. • Leverage Python and application knowledge to interact with and extract data from other systems. • Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance. • Contribute to and help define data engineering standards, best practices, and documentation across the team. • Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

• Lead the design, architecture, and delivery of scalable, Azure-based data platforms • Drive the strategy and execution of scalable, secure, and high-performing data solutions • Oversee data architecture, modeling, and engineering teams • Partner with executive leadership to prioritize data initiatives • Lead the design and implementation of enterprise data architecture • Define and enforce data modeling standards to support scalable analytics and reporting • Oversee the development and maintenance of robust ETL/ELT pipelines integrating data from multiple sources • Ensure data accuracy, consistency, and availability across platforms • Collaborate with Analytics, BI, Finance, Clinical, Operations, and Commercial teams to understand data needs