Data Mesh Experts in Germany

• Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
• Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
• Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
• Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.

Project, Strategic Consulting – building the Data Strategy and Data Governance Policy for the German branch, client (private bank Julius Bär, headquarters Zurich), March 2026 – present

• Design and negotiation of the data strategy with key stakeholders, including obtaining board sign-off (strategic consulting) – in this context, regulatory advice on data regulations in the EU and specifically for Germany. The data strategy includes: Data Lifecycle Management: data capture, data storage, data usage, data retention policy, data quality incident management
• Definition of milestones and technical feasibility for implementing TOM for the data strategy, data quality checks, metrics, and a metadata inventory to ensure the bank’s compliance with DORA, BCBS239, and MaRisk requirements.

Core project data change, client: (ING Bank, Frankfurt am Main), March – December 2025

• Concept development and solution design for new end-to-end processes including technical interfaces
• Definition of synchronization logic and data flows between legacy and target systems (decommissioning of legacy systems)
• Analysis and validation of data models
• Stakeholder communication with product owners, feature engineers, UX designers, and operational teams for decision-making
• Analytics and impact assessments, e.g. to assess downstream effects and regulatory requirements
• Documentation and comments on technical and business requirements to support implementation in agile squads

Project digitalization of a user group, client: (ING Bank, Frankfurt am Main), as Interim Product Owner, Jan 2025 – present

• Co-shaping key decisions on data architecture and process logic in the context of historized data and user login functionality
• Development of business solution concepts for migration to the target system, including system integration and data flows
• Support with analytics and impact analyses, especially regarding the ability to provide information to law enforcement authorities
• Active coordination with stakeholders from different squads to support decision-making and ensure regulatory requirements are met
• Creation of test scenarios for operational teams and backend systems in the area of API management using Postman and Bruno.

Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.

Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows

Consumer Cards Solution Architect

• Provided architectural leadership in Consumer Cards Domain establishing best practices and improving architectural transparency and maintainability by designing a structured documentation framework to enable reverse engineering of legacy card systems.
• Standardized architectural artefacts including BIAN Business Capabilities, UML diagrams in draw.io format (Use Case, Component, Sequence), naming conventions, document repository, design templates and blueprints, microservices.
• Produced high-level and low-level designs aligned with enterprise architecture governance processes and artefact standards.
• Provided architectural support to the Strategic Card Simplification Programme, focusing on card product migrations and application decommissioning across all countries. Agile environments (Scrum/SAFe).
• Analysed and designed AI use cases in the architecture domain.

Project: Payment Card Industry Data Security Standards (PCI DSS) Strategic Programme

• Analysed and documented existing data flows across card products and geographic regions to assess PCI DSS compliance.
• Identified areas involving sensitive data at rest and data in motion requiring encryption or masking, ensuring adherence to PCI DSS requirements.
• Collaborated with security, infrastructure, and application teams to align encryption strategies with regulatory and organizational policies.
• Provided strategic advisory services on data strategy, data governance, data management, data quality, data architecture, data mesh, MEGA HOPEX, DAMA-DMBOK, event-driven architecture, end-to-end data flows and card product harmonization models.
• Ensured solution design alignment with regulatory compliance (BCBS 239, DORA, GDPR) and internal policies.

Project: Denmark ATM Outsourcing Project

Objective: Outsource ATM operations and maintenance to a third-party provider while expanding the Denmark ATM fleet, with Nordea retaining ownership of ATMs and cash for the existing and extended infrastructure.

• Led a cross-functional delivery team (project management, business analysis, and architecture) and documented the as-is ATM ecosystem architecture, including end-to-end data flows, integrations, and internal/external application interfaces.
• Designed end-to-end processes for authorization, reconciliation, and settlement, aligning operating model, controls, and compliance requirements across Nordea and the outsourced service provider.
• Produced high-level and low-level solution designs using standardized UML artefacts (Use Case, Component, and Sequence diagrams) to support vendor onboarding, integration planning, and implementation.
• Ensured architectural alignment and decision-making across enterprise stakeholders and third-party providers, managing dependencies and interfaces in the context of the outsourcing initiative.

• It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
• GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
• Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
• CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment

• Led design, development, and deployment of data solutions supporting a major pharma acquisition for Takeda Pharmaceutical Company, delivering transparency reporting systems across Azure,Databricks (Python and Shell Scripting) platforms.
• Owned,Designed and developed scalable ELT pipelines to process Customer and Product data using Azure, complex SQL, Databricks, and shell scripting, enabling efficient data integration and processing across multiple sources including job orchestration and workflow automation.
• Implemented performance optimization techniques (query tuning, parallelism, workload optimization), improving system efficiency and processing time.
• Applied strong analytical and problem-solving skills to assess technical solutions and support business requirements for compliance and transparency reporting.
• Designed scalable data foundations suitable for downstream analytics and AI workloads.
• Led data quality initiatives by assessing multiple source data, defining quality metrics, and establishing processes for monitoring and continuous improvement.

• Drive enterprise AI transformation through alignment of data platforms, AI capabilities, and governance
• Implement scalable Data Mesh architecture to decentralize data ownership and accelerate AI use case development
• Strengthen AI maturity via structural alignment, clarified decision rights, and transparency models
• Coach leaders, product teams, and engineers in AI-enabled decision-making and data-driven product thinking
• Establish governance models for trustworthy AI and measurable business value
• Design flow-based systems (Kanban / Flight Levels) to sustain AI enablement and ensure measurable business integration at scale
• Integrate KPI frameworks, Jira architectures, and BI reporting to enable evidence-based steering of AI initiatives

• Data Migration Engine / Data Migration from proprietary source systems to SAP/S4 (SAP S/4 HANA Migration cont.)
• Development data authorization Concept
• Conception of system architecture / data architecture / data integration – continuous extensions
• Development conceptional / logical (MDM) data Model - continuous extensions

• Due to an organizational restructuring towards product-based teams, the implementation of a Data Mesh in the area of Digital Supplier Management (DSM) was decided.
• A highly available real-time Data Mesh for providing large volumes of data via Kafka and REST to other products.
• New development of a core supplier management software in a (micro-)service-based architecture with a modern tech stack.
• Technologies/Tools:
• Java 21
• Spring Boot
• Angular
• TypeScript
• Go
• MapR
• Kafka
• Docker
• Google Cloud Platform (GCP)
• Kubernetes
• Helm
• GitLab CI
• Maven
• JUnit
• Mockito
• WireMock
• Cypress
• Keycloak
• OAuth 2.0 & OpenID Connect
• Humio
• Nexus
• Artifactory
• SonarQube
• Renovate
• Git
• Postman
• IntelliJ

• Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
• Performance Optimization of Data Ingestion of ETL Pipeline
• Development of Data Validation using Great Expectations
• Leading of the data migration for two sources exchanges
• Data Modeling in AWS Redshift

MLOps

• Model inference implementation by mlflow and AWS SageMaker
• Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
• Implementatino of Model Registry and artifactory using mlflow
• Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
• Feature importance using mlflow

Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy

• Further development, modularization, and technical modernization of the My AXA customer portal using Scrum.
• Connection of new REST APIs to expand the digital product and service portfolio.
• Splitting a monolithic application into clearly defined business responsibilities.
• Continuous refactoring of complex code parts to improve maintainability.
• Supporting other teams with interface design and introducing WireMock for integration tests.
• Standardizing caching and resilience by introducing Spring Cache and Resilience4J.
• Cleaning up request validation according to JSR-170 and developing a simulation process.
• Further development of the code generator to transform OpenAPI contracts into client libraries.
• Development of dashboards and log file analyses using Loki and Grafana Cloud.
• Monitoring and reporting of production and QA systems.
• Tech stack: Java 21, Spring Boot 3, REST, Resilience4J, Docker, OpenAPI, Grafana Cloud, Jenkins, Tekton, OpenShift, AWS, GitLab.

• Implemented the frontend and backend of a chat application for an AI startup
• Used Go and Dart
• Technologies: gRPC, Flutter, Terraform (IaC)
• CI/CD via GitHub, deployment on Google Cloud Run

• Agile setup of a multimodal analysis platform for training materials (video, audio, documents) using Scrum
• Extraction of context-relevant content based on user profiles & competency dimensions
• Personalized delivery of learning content to boost sales performance
• Close coordination with sales teams & stakeholders to validate features
• Use of Gemini, Whisper, Python & JavaScript, deployment on AWS, Perl for scripting data imports
• Integration into existing tools & CRM systems for smooth adoption
• Technologies used: Python, OpenAI, DB tech like PostgreSQL, CI/CD for Airflow DAGs, FastAPI

• Supporting national and international companies in building data-driven processes, methods, systems, and applications (Data-Driven Company) in data management and agile requirements engineering.

• Identifying essential use cases and (non-functional) requirements to build an architecture on the one hand.

• Optimizing clients' internal processes and developing training plans for new technologies and methods.

• Combining technical know-how (for example, data analysis in cloud data analytics environments through semantic layers) with key soft skills such as agility, teamwork, creativity, and analytical competence.

• Emphasis in data management on efficient use of systems as well as the simple application of technologies for data engineering, data cataloging, virtualization, exploration, and visualization.

• Operationalizing both infrastructure as well as data and mathematical analytical models (DevOps, DataOps, MLOps).

• Acting as a link between architecture, business departments, development, and operations, taking into account essential core areas such as data governance, data security, and data quality.