Description

Bridging Requirements and IT: Finding Your Technical Data, Delivering Traceability on Demand, Statistical tests

The Hidden Gap in Your Engineering Process

Your project manager needs a complete traceability report for a customer audit. What follows: engineers searching through Doors or Polarion databases, checking Catia or Solidworks folders, reviewing Teamcenter BOM projects, analyzing parts characteristics in SAP, quality issues in Opcenter. Someone builds an Excel list. Days pass. Versions get confused.

Three days. Four people. Countless manual steps.

For something that should take 30 seconds.

I Am the Bridge

I combine 15+ years in Requirements and Systems Engineering with deep Data Science expertise. I speak both languages: I understand V-Model and traceability matrices. I also know PostgreSQL, Snowflake, APIs, and Python ETL pipelines.

What I Do

I find your technical data wherever it lives and make it accessible. Through direct database connections, tool APIs, and automated extraction, I build systems that deliver traceability reports at the push of a button.

Typical Results:

- Audit preparation: 3 weeks → 30 minutes

- Change cycles: 3 days → 2 hours

- Traceability coverage: 60% manual → 95% automated

- Engineering capacity: 15-20% time recovered

- ROI: 6-9 months

My Approach

Pragmatic and incremental. Proof of concept in 1-2 weeks. Pilot project in 4-8 weeks. Knowledge transfer included—your team maintains it long-term.

Industries: Aerospace, Automotive, Special Machines, Industrial Automation

Let's talk. Free 30-minute assessment to identify where I can help.

Industry field of expertise

Languages

English
Fluent
French
Native or bilingual
German
Fluent
Russian
Fluent
Japanese
Conversational

Workplace preferences

Can work on-site

Stuttgart (up to 50km), Strasbourg (up to 50km), Frankfurt am Main (up to 50km), Nuremberg (up to 50km), Karlsruhe (up to 50km)

Ineos Automotive GmbH
Digital transformation : Automated Reporting & Data Integration Platform, Snowflake, SAP, Power BI
AUTOMOBILE
December 2019 - March 2026 (6 years and 3 months)
Böblingen, Germany
Reports for digital transformation:

Developed Python and SQL-based data analysis platform integrating multiple enterprise systems, following Agile methods.

Massive support for SAP, Teamcenter, Opcenter, Polarion integration.

Key deliverables:
Automated report generation for Engineering, Quality, Aftersales and Production teams
Direct database connections and API integrations
Visualization via Power BI dashboards and ODBC connectors
Eliminated manual data collection across siloed systems

Impact: Transformed multi-day reporting processes into on-demand analytics, enabling faster decision-making across departments.

Technologies: Python, SQL, Power BI, REST APIs, ODBC, Odata, Snowflake, Azure DevOps
Business intelligence Teamcenter Digital Transformation sap Polarion
Eugen Forschner GmbH
Requirements Engineering & Automated Traceability - Autobus Energy Management Systems
AUTOMOBILE
October 2019 - April 2020 (6 months)
Spaichingen, Germany

Created technical specifications for energy switching control units in commercial buses, managing complex requirements decomposition across product/platform layers and disciplines (Software, Hardware, Mechanical, Project Management).

Technical innovation: Built automated data extraction pipeline from Polarion via PostgreSQL direct access. Developed complex SQL queries identifying edge cases and requirement gaps that manual reviews missed. Connected to Excel/ODBC for project manager dashboards.

Business impact: Project leaders gained instant visibility into coverage gaps and tricky use cases across multi-disciplinary development—transforming days of manual analysis into on-demand insights.
Polarion PostgreSQL Requirements Engineering ODBC Business intelligence
ZF Friedrichshafen
Requirements Engineering & Automated Compliance Reporting - Brake Systems
AUTOMOBILE
April 2022 - September 2023 (1 year and 5 months)
Koblenz, Germany
Managed system-level requirements in DOORS for automotive brake systems, ensuring alignment with Model-Based Systems Engineering (No Magic Cameo) and functional safety standards.

Key achievement: Leveraged database expertise to build automated reporting chains that identified discrepancies between processes and departments—transforming manual quality checks into systematic validation.

Impact: Cross-departmental inconsistencies surfaced automatically rather than discovered late in development, significantly reducing rework and compliance risks.

Technologies: IBM DOORS, No Magic Cameo (MBSE), SQL, automated reporting scripts
Doors SQLite Requirements Engineering Mechatronics Business intelligence