Projects

Projects from regulated, data-intensive industries.

No client names — with situation, approach and outcome.

Pharma & Medical Technology · 5,000–10,000 employees

Databricks platform with GenAI as a shared service

Situation: Several business units wanted to use GenAI without each team building its own infrastructure, models and security concepts.
Approach: Governance with Unity Catalog and Key Vault, hub-and-spoke architecture with Entra IAM, Vector Search and Model Serving as platform services, Azure OpenAI and Document Intelligence as shared services — all in Terraform across DEV, UAT and PROD.
Outcome: One shared platform on which teams deliver new GenAI use cases from existing building blocks.

Transportation & Logistics · 1,000–5,000 employees

Situation: Data from Oracle, SAP, SQL and file shares needed to land reliably in the data lake and Databricks — under enterprise security requirements.
Approach: End-to-end pipelines with ADF and Databricks, performance tuning, CI/CD in GitHub across three environments with self-hosted runners, monitoring with KQL and alerts.
Outcome: Automated, monitored data pipelines instead of manual loads.

Large enterprise · 5,000–10,000 employees

Situation: No central data platform; data from on-premises, Azure and Google Cloud had to be brought together for Power BI and analytics.
Approach: Product owner and data architect: medallion architecture, three-tier landscape, infrastructure as code, company-wide data strategy with policies and governance.
Outcome: A platform and a data strategy the company builds further use cases on.

Automotive · 10,000+ employees

Situation: Business partner data needed to be maintained centrally, validated and distributed to many systems.
Approach: Lead development for SAP MDG-C on S/4HANA, external address and VAT ID validation services, API management, testing and rollout support.
Outcome: One system of record for business partners with automated quality checks.

Industrial Manufacturing · 10,000+ employees

Situation: Engineering and master data had to flow between SAP, a digital twin system, AWS services and databases.
Approach: Solution architecture and development lead, REST and SOAP integration, standardized web services, Fiori prototype for procurement, ETL with Power BI, operations on Linux and Docker.
Outcome: Connected systems instead of silos.

Large enterprise · 10,000+ employees

Situation: Customer master data had to stay consistent across MDM, SAP cloud solutions, SAP Commerce and Salesforce.
Approach: MDM solution architecture, integration of BP Cloud and C4C, Cloud Integration design, and a data quality and upload tool on SAP BTP (Cloud Foundry).
Outcome: Consistent customer master data across SAP and non-SAP cloud systems, bulk maintenance of up to 10,000 customers in one step.