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Design and develop data models, transformations, hubs, lakes, reporting platforms, and warehouses for insurance and reinsurance clients. Analyze business requirements, lead solution development, manage projects or subprojects, and act as a primary client contact. The role requires expertise in data engineering or analytics, insurance data models, stakeholder communication, Agile and DevOps methodologies, and leadership of internal and client teams.

Design and develop data models, transformations, data hubs, lakes, reporting platforms, and warehouses for insurance and reinsurance clients. Analyze business requirements, lead data solution projects, manage client and internal teams, and serve as a subject-matter expert and primary client contact. The role requires insurance data knowledge, stakeholder communication, project ownership, and German and English fluency.

Design and develop data models, transformations, and data platforms for insurance and reinsurance clients. Analyze business requirements, define solution approaches, lead data solution projects, and manage internal and client teams. Serve as a subject-matter expert and primary client contact while supporting migrations and redesigns of insurance business systems. Apply data engineering, analytics, DevOps, Agile, and stakeholder-management expertise.

Design and develop data solutions for insurance and reinsurance clients, including data models, transformations, data hubs, data lakes, reporting platforms, and warehouses. Analyze business requirements, lead solution design, manage project or subject-matter responsibilities, and serve as a primary client contact. Lead internal and client teams while applying data engineering, analytics, DevOps, and Agile practices.

Lead the architecture and design of a scalable data lakehouse, including data models, data flows, ingestion patterns, governance, security, lineage, migration, and integration. Provide technical leadership to data engineering teams and support solution reviews, testing, production readiness, and handover. The role requires strong Azure, Databricks, Delta Lake, data integration, data governance, and cloud architecture expertise, preferably in financial services or another regulated industry.

Develop reusable batch, CDC, and streaming data ingestion and transformation pipelines for enterprise Data Lake and Lakehouse programs. Build Bronze, Silver, and Gold data layers; implement data quality, reconciliation, validation, monitoring, and governed data products. Support migration, integration testing, UAT, production releases, and BAU handover while following cloud, security, governance, and engineering standards. Collaborate with architects, analysts, BI developers, and client technology teams in financial services transformation projects.

Lead large-scale fund administration transformation programs across fund accounting, transfer agency, middle office, and securities operations. Advise senior financial-services clients on strategy, operating models, process redesign, controls, regulatory obligations, and delivery roadmaps. Oversee end-to-end implementation, risk, quality, and technology solutions while mentoring consulting teams and supporting practice growth through sales, business development, proposals, frameworks, and thought leadership.

Designs and maintains scalable batch and real-time data pipelines, ETL/ELT workflows, and Lakehouse infrastructure using Azure Data Services, Databricks, and Delta Lake. Ensures data quality, governance, security, reliability, and performance while collaborating with architects, data scientists, and analysts. Responsibilities include data modeling, lineage tracking, production troubleshooting, code reviews, and delivering model-ready datasets for banking and digital finance use cases.

Consults with financial services clients on business and technology transformation initiatives. Analyzes requirements, system landscapes, application architectures, data flows, data quality, governance, master data, CRM processes, and business processes. Translates complex needs into concepts, specifications, user stories, and recommendations while coordinating Business, IT, Data, CRM, Architecture, and project stakeholders to develop pragmatic solutions.

Design, develop, and deploy production-grade AI applications using LLMs, foundation models, RAG architectures, vector databases, and agentic workflows. Build REST APIs, backend services, and cloud-native systems while integrating APIs, external tools, and enterprise knowledge sources. Collaborate with software engineers, product managers, and AI specialists to translate business requirements into scalable solutions. Apply software engineering best practices, with opportunities to work on multi-agent systems, observability, containerization, and frontend technologies.