Capability radar
- Data Analytics92%
- Consulting90%
- AI & Predictive90%
- Governance88%
- BI & Decision Support86%
- Azure Cloud78%
Company profile
AI Transformation, Data and Analytics Consulting
Markus Stadi
AI transformation expert for business-process optimization in data-intensive, regulated, and operationally complex industries. Combines more than 20 years of leadership experience in IT, data analytics, data engineering, and technical architecture with deep domain knowledge across energy, grid data management, public sector, manufacturing, banking, and cloud transformation. Translates business processes, domain requirements, and data landscapes into concrete AI use cases, robust data architectures, governance structures, and implementation-ready solutions.
Capability profile
Skills at a glance
Experience depth, functional focus, and domain knowledge in one visual profile.
Experience by capability
95%
Public Sector
Proven domain capability
88%
Energy
Proven domain capability
82%
Banking
Proven domain capability
84%
Manufacturing
Proven domain capability
Service profile
AI transformation for measurable process improvement
Consulting for organizations that want to systematically optimize business processes in familiar industries with AI, data platforms, analytics, and governance.
AI Transformation and Business Process Optimization
Compliance, Risk and Fraud Management
Predictive Analytics and Machine Learning
Data Engineering and Integration
Core services
- AI transformation consulting for business processes in familiar industries
- Identification, prioritization, and delivery of economically relevant AI use cases
- Tender framing for data, analytics, and AI workstreams
- Azure and enterprise data platform architecture
- Fraud management, anomaly detection, and predictive analytics
- Power BI, dashboarding, and decision-support products
- Data governance, quality management, and compliance by design
Industry experience
Domain knowledge from real delivery environments
Repeated work inside industry processes, source landscapes, governance constraints, and business decision cycles.
Energy and Utilities
Grid, operations, reporting, and data quality in complex utility landscapes.
Business understanding
Reporting aligned with operational realities, source-system landscapes, quality controls, and business decision support. AI potential lies in data quality, operational steering, prioritization, forecasting, and faster domain decision-making.
Data understanding
SAP-heavy operational data, interfaces, scorecards, dashboards, quality checks, and cloud analytics across distributed enterprise estates.
Proven in data products for the energy and utilities sector.Manufacturing and Industrial Operations
Production, logistics, ERP, MES, and reporting in industrial source-system estates.
Business understanding
Deep understanding of production, logistics, finance, and enterprise reporting, with a focus on operational applications, reliability, and compliance. AI transformation addresses transparency, process stability, automation, and data-driven steering.
Data understanding
Integration of SAP, CRM, MES, logistics, Jira, flat files, and cloud services into reporting-ready models and governance-aware architectures.
Proven in an Azure cloud data-lake migration for the manufacturing sector.Banking and Regulated Finance
Governance-heavy reporting, traceability, compliance, and risk-aware data integration.
Business understanding
Understanding of banking steering, management reporting, compliance expectations, and disciplined delivery in regulated environments. AI transformation is linked to traceability, controllability, and robust data processes.
Data understanding
MaRisk, ICT, ITR, Collibra, Ab Initio, Oracle, and cross-system traceability requirements.
Proven in a banking data-integration and governance platform.Public Sector and Government Platforms
Public-sector analytics, fraud control, data integration, and resilient digital services.
Business understanding
Broad knowledge of business processes across labour-market, pension, migration, and family-benefit services, together with comprehensive knowledge of central application systems, including master-data and financial applications. AI transformation starts with process quality, case steering, audit automation, and decision support.
Data understanding
Fraud analytics, governance, public-sector warehousing, predictive models, and complex financial and operational systems.
Proven through long-term delivery for public employment services and public-sector architecture programs.Skills matrix
Core AI transformation and data capabilities
CV-backed capabilities ranked for AI transformation, process optimization, and data consulting in familiar industries.
| Capability | Category | Experience |
|---|---|---|
| AI Transformation and Process OptimizationAnalyze industry-specific business processes, identify viable AI use cases, and translate them into prioritized roadmaps, target architectures, and delivery-ready work packages. | AI Transformation | 20 years |
| Tender Solution FramingTranslate complex data, AI, governance, and platform requirements into delivery-ready scope, architecture, and implementation narratives. | Consulting | 15 years |
| Business Intelligence and Decision SupportCreate reports, scorecards, dashboards, and management-facing analytical products with strong emphasis on business usability and trust. | BI | 14 years |
| Data Governance and ComplianceStrengthen governance, control systems, data quality, and auditability across regulated environments with traceable analytical processes. | Governance | 10 years |
| Data Engineering and IntegrationBuild and modernize data platforms, ETL pipelines, data products, and cross-system integrations across cloud and enterprise estates. | Data Platform | 12 years |
| Predictive Analytics and Machine LearningDesign, prototype, and operationalize predictive models for scoring, classification, anomaly detection, and decision support. | Machine Learning | 10 years |
| Azure Cloud Data PlatformsDesign secure Azure analytics platforms with Synapse, Data Factory, Databricks, ADLS2, private endpoints, and governance controls. | Cloud | 6 years |
| Compliance, Risk and Fraud ManagementDevelop fraud analytics, audit-focused controls, and anomaly-detection workflows across payment, financial, and benefits environments. | Risk AI | 10 years |
| Python, PySpark and Analytical EngineeringWork hands-on with Python, PySpark, pandas, scikit-learn, and notebook-based development for robust analytical workflows. | Engineering | 6 years |
Selected engagements
Industry delivery with business context
Projects where domain knowledge, architecture, governance, reporting, and stakeholder understanding came together.
Data Products for Energy and Utilities
Energy and Utilities
Design and implement reporting, scorecards, dashboards, data interfaces, and data-quality checks for a complex utility environment.
Business context
Reliable data products and practical analytics were required across multiple energy and utility source systems.
Data landscape
Selected delivery
- Reporting, scorecards, dashboards, and interfaces
- Data-quality checks for SAP and adjacent systems
- GPT-5.x, Codex, and Databricks Genie-assisted implementation
Azure Cloud Datalake Migration in Manufacturing
Manufacturing and Industrial Operations
Migrated a data-warehouse landscape to an Azure cloud platform with Synapse, ADLS2, serverless SQL pools, and CI/CD.
Business context
The reporting estate needed a modern target unifying SAP, CRM, MES, logistics, and operational sources.
Data landscape
Selected delivery
- Cloud data-lake architecture for reporting and analytics
- Enterprise and operational source integration
- Purview proofs of concept and governance-ready patterns
Banking Data Integration and Governance Platform
Confidential banking and real-estate environment
Built a data-integration platform for central steering and reporting under MaRisk, ICT, and ITR requirements.
Business context
Reliable cross-system reporting was needed with stronger governance, traceability, and integration discipline.
Data landscape
Selected delivery
- Management and operational reporting integration
- Governance-heavy quality controls
- Traceable banking data-platform architecture
Team Lead Data Analytics & Governance / Data Architect
Public Employment Services · Public administration / financial and insurance services
Led and held functional responsibility for an analytics and data-science team of approximately ten people in Enterprise Fraud Management, coordinating internal and external data scientists.
Business context
Supported IT governance, compliance, and the internal control system through data-driven reviews of process, financial, and data architectures and interfaces within the Three Lines of Defence model.
Data landscape
Selected delivery
- Owned data, analytics, and technical concepts, including analysis of master-data systems, transaction systems, and interfaces as well as data modelling and data-quality analysis
- Designed and directed financial monitoring, financial analytics, anomaly detection, sampling, impact analyses, dashboards, heatmaps, and audit workflows
- Supported the introduction of SAP HANA, SAP Fraud Management, and SAP GRC, including transition of existing analytical models and management of external service providers
- Served as Lead Data Scientist for a forecasting proof of concept covering information-needs analysis, source selection, feature engineering, model design, and management alignment
Business Intelligence and Data Warehouse Architecture
IT Services
Led architecture and delivery for data warehouse, service-management reporting, and BI modernization programs.
Business context
The programs required robust warehousing, KPI reporting, migration planning, and high operational reliability.
Data landscape
Selected delivery
- Technical leadership for complex DWH and BI workstreams
- Fact models, dimensions, ETL packages, and reporting services
- Public-sector and automotive analytics delivery
Senior IT Consultant / Data Warehouse Workstream Lead
IT Consulting, Nuremberg · Public sector
Requirements analysis, ER modelling, structured analysis and design, and workstream leadership in a complex data-warehouse project with a team of up to four people.
Business context
Designed and implemented DWH architecture and business-intelligence solutions for operational units and controlling, with substantial technical delivery responsibility and a focus on timely reporting.
Data landscape
Selected delivery
- Produced detailed technical specifications, architecture standards, and component-level designs for the data warehouse
- Integrated new data sources, modelled data marts and transformations, and developed ETL packages for a bitemporal data warehouse
- Performed data and impact analyses, data-quality controls, reporting, third-level support, and continuous maintenance of interfaces to operational systems
- Assumed responsibility for master data management and consolidation of master data from systems of record from March 2010
- Planned, controlled, and tested implementation while leading a team of up to four people
Case studies
Business and data understanding in context
AI Transformation in Operational Business Processes
Business challenge
Business functions need concrete AI opportunities that do not stop at ideation workshops, but connect process value, data availability, governance, and feasibility.
Data and delivery approach
Business processes, data landscapes, and decision cycles are analyzed, AI use cases are prioritized, and the results are translated into architecture, data-product, and delivery roadmaps.
Outcome
A robust transformation portfolio with realistic AI initiatives, clear business impact, and a technical implementation basis.
Data Products for Energy and Utilities
Business challenge
Multiple source systems, operational stakeholders, and governance expectations made fast analytical delivery difficult.
Data and delivery approach
Combined Power BI, Azure, Databricks, and structured quality checks with strong stakeholder alignment across the utility landscape.
Outcome
A more robust decision-support layer and a clearer delivery foundation for future analytics and AI initiatives.
Azure Cloud Datalake Migration in Manufacturing
Business challenge
The target architecture had to support broad source-system integration while meeting security and operational constraints.
Data and delivery approach
Designed the Azure platform end to end, integrated multiple domains, and delivered reporting products for finance, logistics, HR, and production.
Outcome
A scalable modern data foundation aligned with reporting needs and future governance requirements.
References
Industries and delivery environments
Energy Grid Operations
Energy Grid Operations
Senior Performance and Project Manager
Energy data products and quality managementEnergy and Utilities
Energy and Utilities
Enterprise source-system landscape
Energy and utility analyticsManufacturing and Industry
Manufacturing and Industry
Technical Consultant Datalake and Data Architect
Azure cloud migration and reporting modernizationPublic Employment Services
Public Employment Services
Team Lead Data Analytics & Governance / Data Architect
Enterprise fraud management and IT governanceIT Services
IT Services
Technical Project Lead
BI and data-warehouse architecturePublic-Sector IT Consulting
Public-Sector IT Consulting
Senior IT Consultant / Workstream Lead
Public-sector DWH and master data managementIT Consulting and Sporting Goods Retail
IT Consulting and Sporting Goods Retail
Business Information Management
Retail segmentation and analyticsIndustry labels are used to present project references anonymously.
AI transformation and process optimization
Turn business processes into implementation-ready AI solutions.
Feature-Engine supports organizations from use-case identification, process analysis, and architecture through hands-on data, analytics, governance, and AI delivery.