Data Lineage Experts in Germany

Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.

Financial Data Integrity & ERP Governance

• Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
• Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
• Validated SAP reports, establishing baseline data quality standards for Finance team consumption
• Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs

Finance Reporting Transformation

• Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
• Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
• Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
• Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models

Power BI & Analytics Enablement

• Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
• Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
• Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team

Transformation Infrastructure & Collaboration

• Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
• Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
• Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization

Outcomes

• GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
• Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
• Power BI transformation roadmap presented and approved by Finance leadership
• Jira-based project governance live; Finance transformation now tracked with full sprint visibility

Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python

• 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.

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

UEF 3.0 · Semantic Government Overlay (SGO) · Autonomous Systems (UAS / dual use)

• Designed: Semantic Government Overlay (SGO) – AI-guided administration without replacing existing specialist procedures. Read-only semantic layer over registers and specialist processes based on the Federal Information Management (FIM). Decision authority remains with the case worker (architecture principle).
• Developed: Reference architecture with source-backed, derived statements (Executable Ontologies OWL/RDF/SHACL). Technically guaranteed purpose limitation and no-write-path principle in specialist data – auditable, without a central data pool.
• Anchored: Regulation as a design principle: EU AI Act (high-risk obligations for public-sector AI, fundamental rights impact assessment under Art. 27), GDPR, NIS2, and administrative automation limits (§ 35a VwVfG, § 31a SGB X) as technical control points in the architecture.
• Created: Methodical tool for pilot organizations: data pipeline assessment (phase 0), compliance blueprint, and management summary as a decision-ready package for public administration.
• Specified: UEF 3.0 as a successor architecture to TOGAF – decision paper, canonical ontology, six-layer architecture, read/actuate boundary, federation registry, terminology concordance, and release delta as a closed specification status.
• Architected: AI-native mission OS for autonomous UAS and ground robotics as a tactical layer on top of a separately approved autopilot. Run-time assurance according to ASTM F3269-21 (Simplex pattern): the verified safety controller keeps authority, the AI function provides suggestions.
• Designed: Three-tier architecture – Tier 0 autopilot with 650 Hz flight control on RTOS, Tier 1 AI OS with semantic world model and multi-agent cluster, Tier 2 swarm and ground mesh. Zenoh as the primary fabric, MAVLink as the only authenticated command path (single writer). Result: graceful degradation – loss of the mission, not of the aircraft.
• Secured: Two-gate chain on the read/actuate boundary – governance gate (can-question: AI Act risk class per actuation, enforced human oversight under Art. 14, immutable log) before the RTA safety monitor (is-it-correct question: flight envelope, geofence, energy reserve) with revert to the baseline controller.
• Anchored: Dual-use architecture with common core and build-time fork instead of runtime switch. Three separate legal levels: civil variant – UAS under the EASA Basic Regulation (EU) 2018/1139 with the limited applicability under Art. 2(2) of the AI Act, ground robotics under the Machinery Regulation 2023/1230 with the full high-risk obligation chain, Cyber Resilience Act for both; unarmed carrier variant as defense material under AWG/AWV and Dual-Use Regulation 2021/821 (BAFA approval); armed variant under KrWaffKontrG. Each variant lives under exactly one dominant legal regime. Evidence base: AI BOM, SBOM, and complete data lineage.
• Analyzed: System analysis and realignment of grown engineering system landscapes. Approach concept for consolidation without migration – semantic layer over the existing sources instead of data transfer. Result: decision-ready implementation concept including an evaluation model for the target architecture.

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.

Clients: CARIAD, AUDI AG

• Built and deployed enterprise MLOps pipelines using Azure Machine Learning and Databricks. Reduced model release cycles by 95 percent, from four weeks to two days, through automated CI CD workflows.
• Delivered cloud native DevOps platforms for ADAS programs using Azure data services, Kubernetes, and Terraform based infrastructure provisioning.
• Designed high availability architectures with automated failover. Cut system downtime by 85 percent for mission critical energy trading platforms.
• Developed and integrated AI agents and enterprise chatbots using LangChain, AutoGPT, and GPT models. Enabled autonomous workflows and decision driven automation.
• Reduced cloud infrastructure spend by 40 percent through autoscaling strategies, spot instance usage, and policy driven resource governance across Azure and AWS.
• Partnered with Data Scientists, ML Engineers, Product Managers, and executive stakeholders to deliver large scale automotive and energy solutions.
• Implemented GitOps driven CI CD pipelines supporting automotive software delivery for over 500 engineers across distributed product teams.
• Designed and operated Kubernetes platforms on Azure AKS. Improved deployment stability and reduced rollback events by 70 percent.
• Implemented observability and monitoring stacks using Prometheus, Grafana, and Azure Monitor. Achieved 99.9 percent service availability targets.

I implemented a Zero-Trust architecture for robust, military-grade maritime container mini data centers based on VMware & Tanzu to support containerized GIS workloads for ground forces. The main focus was on securing communications, workload protection, and data access in contested electronic battle environments affected by jamming, interception, signal manipulation, and constantly changing operational conditions. I designed and architected use cases so that every element of workload, identity, and system could continue to operate independently and securely even in degraded or disrupted scenarios. In parallel, I defined the enterprise and solution security architecture with LeanIX, Bizzdesign, and HOPEX as enterprise architecture, repository, and governance platforms to maintain architecture inventory, relationships, traceability, target pictures, and security governance in complex environments. For the architectural designs, I used Sparx Enterprise Architect to describe formal architecture views, interfaces, trust boundaries, and system architecture in both IT and OT environments. IriusRisk was used for threat modeling of the solution to identify architecture-driven risks, derive security requirements, and detect countermeasures and design gaps directly from the solution models. Risk and compliance management was supported with Archer. Architecture decisions, control gaps, and operational risks were translated into controlled governance and auditable compliance measures. For documentation, collaboration, and visual design, I used Confluence to maintain Architecture Decision Records, Security Blueprints, and workflows. I used Lucidchart and draw.io to create design artifacts tailored to stakeholders. I also defined OT security concepts with support from electrical and mechanical engineers in the areas of oil, vehicle onboard systems, rail, power plants, pharma, gas turbines, and nuclear technology. I created the end-to-end OT security strategy, starting with global policy, developed into standards and procedures, and finally aligned with Bell-LaPadula, Purdue Model, SABSA, TOGAF ADM, CENELEC 50701, IEC 62443, and NIST standards. In addition, I worked with engineering team leads to identify critical KBP assets and place them under protective measures that segmented SCADA, PLC, and HMI assets. I drove collaboration between Security, IT, and OT teams to create standardized workflows and use cases for the OT security solution catalog, while integrating Defense-in-Depth and Zero-Trust principles into operational environments. A key part of my work was integrating multidisciplinary engineering, security, and operations stakeholders into a unified security blueprinting strategy and ensuring that architecture, threat modeling, governance, and documentation were technically strong and operationally practical.

• Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
• Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
• Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
• Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
• Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
• Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
• Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
• Created and maintained dashboards in Power BI to provide actionable insights.

I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.

I delivered following projects and engagements as a freelancer.

• Data Migration of CRM System for AL-FA Objekt Service Gmbh
• Microsoft Software Resales Partnership

I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer

Technical Focus Areas

• Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
• DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
• Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
• MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
• Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.

• Designed a future-proof client database for marketing purposes
• Analyzed requirements, designed, and modeled an entity-relationship model
• Consolidated and optimized a client file from various data sources for targeted marketing campaigns
• Worked closely with marketing and IT in an agile environment to iteratively develop the solution
• Technologies and methods: MS Office (mainly Excel), MS Dynamics CRM, MS SharePoint

• Led cloud and data engineering organization, defining architecture strategy for next-generation data platforms
• Designed and delivered an automotive fleet data management system including scalable ingestion pipelines, signal catalog management, and campaign processing workflows
• Built cloud-native microservices and streaming architectures supporting real-time vehicle data and AI-powered threat detection
• Established engineering standards for data quality, security, lineage, and governance in alignment with ISO/SAE 21434 and GDPR
• Managed engineering teams across data, backend, cloud, and AI functions, ensuring consistent delivery of high-quality, production-ready solutions

• Led end-to-end implementation of the Miles and More Credit Card Service integrating with TSYS systems on Microsoft Azure
• Architected secure, scalable APIs and microservices using Spring Boot, Camunda, and Apache Kafka (Confluent Platform) for real-time event streaming and process automation on Azure Kubernetes Service
• Managed full development lifecycle of the credit card system on Azure Cloud including fraud detection, payment processing, account management, and Kafka-based event-driven data pipelines
• Designed and implemented Kafka Connect integrations and Schema Registry for seamless data flow between microservices and legacy systems
• Introduced Agile best practices, improving team efficiency by 30% and reducing project delivery time
• Directed and mentored a team of 15+ developers fostering a collaborative, cloud-first, and innovation-driven culture
• Managed client relationships achieving 95% satisfaction and securing follow-up projects through consistent delivery excellence
• Designed and deployed BPMN/DMN models in Camunda for complex business workflows and automation
• Configured Camunda Engine with advanced features such as error handling, RPA integration, and multi-version deployments
• Integrated Camunda and Kafka-based messaging with core banking systems ensuring high availability and data consistency
• Managed multi-database schema configurations on Azure and optimized system performance for high throughput and fault tolerance
• Designed and implemented cloud architecture patterns on Azure focusing on multi-region resilience, data sovereignty, and event-driven scalability for critical financial workloads

• Built an enterprise data lakehouse platform on Azure Databricks

• Developed production data pipelines and governance structures

• Implemented private cloud infrastructures using Terraform

• Introduced modern CI/CD standards in Azure DevOps

• Implemented secure IAM and governance concepts

• Developed scalable PySpark and Delta Lake frameworks

• Supported self-service analytics and data product approaches

• Provided architecture and platform consulting for enterprise data initiatives

• Built a central DataHub architecture for insurance data

• Integrated multiple subsystems into a lakehouse platform

• Introduced data governance and data lineage

• Supported modern analytics and reporting standards

• Optimized data delivery for business and analytics teams

• Topics: Business Analysis, Data Privacy, Data Management, Stakeholder Management, Conceptualization
• This project focuses on developing and implementing a strategic approach for data deletion in SAP systems. The goal is to identify the relevant data and structures during system migration to ensure both data privacy and IT system efficiency. At the same time, downtime should be minimized and regulatory requirements met.
• Development of a comprehensive approach for data deletion in SAP systems, considering data privacy and business requirements.
• Ensuring efficient and structured data transfer to the new system.
• Optimizing system efficiency and reducing downtimes during migration.
• Creating functional and technical concepts to ensure compliant and sustainable data management.
• Topic preparation: Detailed study of the "data deletion" area to lay the foundation for a structured data migration.
• Definition of project structure: Setting roles, interfaces and the project's organizational structure.
• Regulatory requirements: Analysis of data privacy regulations and business requirements to define deletion criteria.
• Approach: Developing possible scenarios and methods for data cleansing and deletion.
• Deletion concepts: Creating functional and technical deletion concepts that structure the implementation and provide clear guidelines.
• Setting deletion criteria: Defining which data and structures to delete or transfer.
• Responsibilities: Clarifying responsibilities within the project team and among stakeholders.
• Analysis of ongoing activities: Identifying and collecting existing activities in the "data deletion" area.
• Effort, cost and timeline planning: Creating estimates for resources, effort and budget.
• Implementation initiatives: Developing and executing concrete measures to apply the defined deletion strategies.
• IT system efficiency: Analyzing the existing IT infrastructure to identify optimization potential for data deletion and transfer.
• Technology trends: Evaluating new technologies and tools that can support the data cleansing process.
• Cost-benefit analysis: Assessing the financial impact of data cleansing and the introduction of new solution approaches.
• Risk management: Identifying potential risks during implementation and developing appropriate mitigation measures.
• This project lays the foundation for a sustainable and compliant data transfer to a new SAP system. With a clear approach to data deletion, it meets data privacy requirements, reduces downtimes and increases the efficiency of the new system. The results and recommendations will help companies develop a future-proof data strategy that meets legal and business needs.