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Hi, I'm

Christoph Lengowski

I connect test management, requirements engineering and enterprise AI integration into reliable delivery systems – in complex projects where both matter: leadership experience and technical currency.

Quality AssuranceTest ManagementRequirements EngineeringTest AutomationAgile Delivery
9QA team members led
RAGEnterprise knowledge system live
5+Years of delivery ownership
Christoph Lengowski

What I Bring

Experience at the intersection of requirements, quality engineering, test strategy, and reliable product execution.

QA Leadership & Test Management

Building and steering QA processes in complex projects. Risk-based test strategy, governance, and quality reporting at management level.

Test Automation & CI/CD

Designing automatable test assets, structuring Cucumber/Gherkin scenarios, and working closely with developers on Playwright implementation. Embedded in CI/CD and delivery workflows with Jenkins, Jira/Xray, and Bitbucket.

Requirements Engineering

Requirements analysis, specification work, and translation of fuzzy business input into testable, prioritized, and reviewable delivery artifacts.

AI-Assisted Workflows & Tooling

Own workflows, tools, and prototypes for requirements, analysis, and QA work. I use AI here as an extension of my delivery and quality practice, not as a replacement for it. That includes cost awareness: relevant context instead of large prompts, caching stable system parts, and routing simple requests.

Agile Delivery

Turning manual or fuzzy processes into clear delivery flows with release readiness, ownership, and reviewable implementation artifacts.

Enterprise AI Integration

Building RAG knowledge systems, n8n automation workflows, and local LLM infrastructure in enterprise environments. AI as a lever for delivery processes: test case generation, requirements work, and knowledge access across Jira, Confluence, and SharePoint.

Certifications

Verified credentials and methodological foundations with a strong focus on test management, requirements, and delivery.

ISTQB logo
2026

Certified Tester GenAI

Certible
  • AI-assisted testing: understanding practical uses of generative AI in software testing
  • Prompting and test design: deriving test scenarios, test data, and test ideas with GenAI support
  • Risk awareness: critically evaluating AI outputs, quality safeguards, and responsible tool usage
ISTQB logo
2026

ISTQB Advanced Level Test Management (CTAL-TM)

ISTQB
  • Strategic test planning: defining and steering test strategies plus risk management for complex systems
  • Team leadership and governance: leading test teams, monitoring KPIs, and improving test processes
  • Commercial focus: estimating effort and controlling budgets to maximize QA return on investment
ISTQB logo
2025

ISTQB Foundation Level

ISTQB
  • Standardized methodology: strong command of the fundamental test process and internationally recognized terminology
  • Holistic test design: applying black-box and white-box test design techniques to find defects effectively
  • Quality mindset: ensuring high software quality through early testing activities across the SDLC
Professional Scrum Master I badge
2021

Professional Scrum Master I

Scrum.org
View certificate

Proof ID: 703391

  • Servant leadership: facilitating Scrum events and removing impediments to maximize team productivity
  • Agile transformation: reinforcing transparency, inspection, and adaptation across the organization
  • Coaching: helping the team self-organize and live the Scrum values and principles
Professional Scrum Product Owner I badge
2024

Professional Scrum Product Owner I

Scrum.org
View certificate

Proof ID: 991278

  • Business value maximization: prioritizing the product backlog strategically to optimize value
  • Stakeholder management: bridging business requirements and technical implementation effectively
  • Product vision: shaping clear product goals and measurable acceptance criteria
IREB CPRE Foundation Level badge
2021

IREB CPRE Foundation Level

IREB

Proof ID: 21-CPREFL-197026-20

  • Precise requirements analysis: eliciting, documenting, and validating functional and quality requirements professionally
  • Conflict management: moderating between stakeholder interests to avoid misaligned implementation
  • Specification excellence: producing clear, testable requirements for smoother delivery
PRINCE2 wordmark
2021

PRINCE2 Foundation

PRINCE2 / PeopleCert

Proof ID: GR656228305CL

  • Structured project management: understanding process-oriented methods for controlled project delivery
  • Business case focus: continuously validating the business justification throughout the project lifecycle
  • Roles and responsibilities: defining clear structures and escalation paths for efficient project execution
Profile

About Me

I work at the intersection of QA leadership, requirements engineering, quality review, and reliable delivery in complex project environments.

I am a Senior IT Consultant and QA Team Lead with solid experience across test management, requirements engineering, and quality steering. In complex project environments, I take responsibility for test strategy, automatable test design, quality review, and release readiness with a clear view of risk, stakeholders, and execution reality.

The difference is in context: I have embedded RAG knowledge systems, n8n automation flows, and AI-assisted artifact generation into live delivery processes – not as a side project, but as an operational part of an enterprise client engagement.

In parallel, I build my own AI-assisted workflows, requirements and quality workspaces, and technical prototypes. That builder practice extends my profile considerably, but it does not replace my core: dependable quality and delivery in real projects.

Agents & AI Systems

The AI systems I build work in real project contexts: with clear input paths, guardrails, traceability, and direct integration into Jira, Confluence, and SharePoint.

  • I translate domain logic, roles, and decision points into usable AI interactions instead of generic chat flows.
  • I design guardrails, validation, data flows, and traceability into the system from the start.
  • I build agents as embedded workflow components, not as isolated gimmicks.
Experience
5+ Years Delivery & QA
Role
QA Lead & Team Lead
Foundation
M.A. + Scrum/ISTQB
Builder profile
Builder & Product Practice
Christoph Lengowski

Quick profile

  • QA team leadership and delivery ownership in complex public-sector programs
  • Strong hands-on practice in test management, quality steering, release readiness, and automation handoff
  • Hands-on product engineering for AI-assisted SaaS, workflow, and quality-workspace products
  • Strong bridge between requirements, quality steering, test strategy, and technical execution
  • Focus on systems that stay reliable from prototype to operating reality

What teams value

  • I bring structure into ambiguous problem spaces and surface risks early.
  • I treat quality as part of requirements and delivery, not as a late control step.
  • I use AI deliberately where it concretely improves test, analysis, and delivery work.

Experience

Career milestones that shaped my profile.

12/2023 - Present

QA Team Lead

Materna Information & Communications SE

Key Impact

QA leadership and test management in a large public-sector program with team ownership, governance, test strategy, and close collaboration on automation in delivery-scale workflows.

Leading a QA team with clear test-management responsibility. Ownership for risk-based test strategy, automatable test design, quality steering, and release readiness in a large-scale public sector project.

  • Built and led a QA team
  • Defined a risk-based test strategy and durable test concepts
  • Structured automatable test cases in Jira/Xray and Cucumber for Playwright handoff
  • Worked with developers on Playwright implementation and Jenkins-based CI/CD integration
  • Stakeholder management and quality governance at project leadership level
12/2021 - 12/2023

IT Consultant

Materna Information & Communications SE

Key Impact

Built durable requirements and delivery structures for real-world digitalization projects and supported implementation through close functional alignment.

Consulting and hands-on work in digitalization projects with a clear focus on requirements engineering. Ownership for requirements intake, functional alignment, backlog structuring, and prioritization as the bridge between client, business stakeholders, and development.

  • Requirements intake and functional analysis translated into actionable backlog items
  • Alignment with clients, business stakeholders, and developers across scope, requirements, and priorities
  • Ownership for structuring, maintaining, and prioritizing the product backlog
  • Creation of functional specifications and implementation-ready delivery artifacts
  • Occasional functional testing and review work from a requirements perspective
Q4 2021 - 12/2021

IT Consulting Trainee

Materna Information & Communications SE

Key Impact

Built the foundation for public-sector project work through training, certifications, and a structured understanding of project delivery.

Structured trainee program for entering project delivery environments with a focus on methodological foundations, delivery contexts, and IT consulting practice.

  • Intensive onboarding into project contexts, delivery flows, and consulting practice
  • Comprehensive training in Scrum, requirements engineering, and project methodology
  • Completed key certifications as a methodological foundation
  • Fast transition from the trainee program into operational project work

Projects

Selected projects and workspaces that make my approach to requirements, quality, and execution tangible.

01Key Project

E-Gov Workflow Platform – Large-Scale QA in the Public Sector

QA Lead / Test Manager for a complex e-government platform

QA leadership with a builder profile

Team leadership, test strategy, test management, automation collaboration, release steering, and deliberate use of AI to support test case and test data work.

8

QA team members led functionally

4

System domains covered

CI/CD

Automation handoff embedded in Jenkins and Bitbucket workflows

Public Sector

E-government delivery in a highly regulated environment

A large-scale e-government platform for digital files and process handling in public administration. Within this complex program, I was responsible for planning, steering, and evolving the entire quality assurance setup, raising QA maturity both operationally and methodologically.

My role

QA Lead / Test Manager / IT Consultant

Tech Stack

Playwright · Cucumber / Gherkin · Jenkins · Bitbucket

Challenge

The project ran in a highly complex public-sector environment with multiple clients, backend services, strong traceability requirements, and demanding release expectations. Quality had to be controlled not just through execution, but through risk-based test strategy, defect governance, test steering, and stakeholder reporting.

Solution

I built a structured test organization, led the QA team, and tightly connected test management, automatable test design, and KPI-based reporting with engineering, project leadership, and client stakeholders. I worked with Jira/Xray and Cucumber-based test assets, supported their handoff into Playwright implementation with developers, and embedded the resulting automation in Jenkins- and Bitbucket-supported delivery workflows. In addition, I used AI deliberately through prompt and context engineering to generate, expand, and plausibility-check test cases and test data faster. That turned quality into a controllable delivery capability with clear release readiness instead of a reactive bottleneck shortly before releases.

Project context

  • Further development of a complex e-government platform for digital files and administrative case workflows
  • End-to-end QA responsibility across Web Client, Outlook Client, Admin Client, and backend services
  • Delivery in a public-sector environment with high expectations around quality, security, and auditability

Project scope

  • Functional leadership of a QA team of up to 8 people
  • Setup and steering of the full test management process
  • Creation of test concepts and test strategies
  • Structuring automatable test cases in Jira/Xray and Cucumber for developer handoff
  • Planning, prioritization, and execution of release and regression testing
  • Ownership of the defect management process
  • Support for government-side test activities plus workshops and training sessions
  • Coverage of accessibility and data protection requirements within the test scope

Impact

  • Built a structured test organization inside a large e-government program
  • Built a risk-based test strategy across multiple system domains
  • Introduced and expanded Playwright- and Cucumber-based automation in close collaboration with development
  • Established defect governance and KPI-based quality reporting
  • Supported integration of automated tests into Jenkins and Bitbucket delivery workflows
  • Used AI deliberately for test case and test data work through prompting and context engineering
  • Owned release and regression steering in a complex public-sector environment
  • Improved release stability through systematic quality steering
  • Established test KPIs and reporting for leadership and stakeholders
  • Covered accessibility and data protection requirements as part of the QA scope

Tech Stack

PlaywrightCucumber / GherkinJenkinsBitbucketJiraXrayConfluence.NET / C#SQL ServerWebservicesSharePointOutlook Add-in
02Key Project

Enterprise AI Integration – RAG Knowledge System and Delivery Automation

AI integration into live enterprise delivery processes

Design and build of an internal AI knowledge system based on LightRAG, Open WebUI, and local LLM infrastructure. Complemented by n8n automation workflows for AI-assisted generation of user stories, test cases, and requirements artifacts, with direct integration into Jira, Confluence, and SharePoint as an AI-powered delivery pipeline.

My role

AI Integration / IT Consultant

Tech Stack

LightRAG · Open WebUI · n8n · Lokale LLM-Infrastruktur

Challenge

Teams in complex projects struggle with knowledge silos, repetitive artifact generation, and the manual translation of large-scale project documentation into usable deliverables. Generic GenAI tools are of little help here, as they lack project context and cannot operate reliably without a structured knowledge base.

Solution

I designed and built an internal RAG knowledge system that stores technical project documentation (requirements, specifications, test artifacts, process diagrams) as a structured knowledge base and makes it accessible through context-informed queries. In parallel, I developed n8n workflows that use Jira, Confluence, and SharePoint as input sources, generate requirements artifacts and test cases with AI support, and hand off results directly into live QA and delivery processes. I continuously optimized the knowledge graph by resolving semantic inconsistencies and standardizing terminology.

Highlights

  • Built a RAG knowledge system using LightRAG, Open WebUI, and local LLM infrastructure
  • Structured and curated technical project documentation (requirements, specifications, test artifacts) as a knowledge base
  • Developed n8n automation workflows for AI-assisted generation of user stories, test cases, and requirements artifacts
  • Integrated Jira, Confluence, and SharePoint into AI-powered delivery pipelines
  • Optimized the knowledge graph by resolving semantic inconsistencies and standardizing terminology
  • Embedded the system as a workflow component within live QA and requirements processes rather than operating it as a standalone tool

Learnings

Enterprise AI demonstrates its value not in prototypes, but when retrieval quality, system boundaries, and process integration are thought through from the start. Curating the knowledge base matters at least as much as the infrastructure beneath it.

Tech Stack

LightRAGOpen WebUIn8nLokale LLM-InfrastrukturRAG-ArchitekturJiraConfluenceSharePointKnowledge Graph
03Key ProjectVisit Website

Requirements & Quality Workspace

AI-assisted workspace for discovery, quality review, and test strategy

A full-stack product for product owners, business teams, and delivery setups that turns vague ideas into reviewable requirements, quality analysis, traceability links, and test-oriented artifacts. After merging the former Teststrategy Generator into the same product, the workspace now covers structured discovery, requirements critique, test-strategy workflows, Jira test case export, and shareable PDF artifacts inside a production-oriented Next.js architecture.

My role

projects.requirements-tool.role

Tech Stack

Next.js 16 · React 19 · TypeScript · Claude API

Challenge

The friction between business context and implementation is rarely about lack of expertise. Stakeholders know the problem, but often express requirements too vaguely or too inconsistently, which costs teams time, scope clarity, and quality.

Solution

I built a guided three-phase interview flow with context-aware follow-up questions and AI-assisted consolidation, then extended it into a reviewable quality workspace. The system now produces prioritized user stories, acceptance criteria, NFRs, and open questions, while also critiquing requirement quality, building traceability, generating test-strategy artifacts, and exporting test-case-ready outputs into Jira-aligned delivery flows.

Highlights

  • Next.js 16 app with React 19, TypeScript, and Tailwind CSS v4
  • Guided three-phase interview flow instead of blank requirement forms
  • Claude-generated summaries and critique before final generation
  • Artifact bundle with requirements, epics, features, acceptance criteria, and NFRs
  • Traceability, quality-gate, and test-strategy workspace in one product
  • Jira test case export for Zephyr/Xray-oriented delivery flows
  • PDF export for directly shareable requirements and review artifacts

Learnings

Better requirements work does not stop at writing. The real leverage appears when discovery, quality review, traceability, and test strategy run in one workflow instead of being split across separate tools.

Tech Stack

Next.js 16React 19TypeScriptClaude APIRequirements AnalysisTraceabilityTest Strategy WorkspaceJira Test Case ExportPDF Exportshadcn/uiTailwind CSS v4
Own Projects

More Own Projects

7 projects

Reusable Assets & Accelerators

Reusable workflows and delivery assets that help teams move from vague requests to reviewable outcomes faster.

Reusable AssetLLM QARelease GatesPlaywright

AI QA Release Gates

A reusable QA framework for AI features with traceability, multi-model tests, and explicit release criteria before shipping.

Best suited for

Teams that want to move AI features beyond prototyping and qualify them as measurable, reliable delivery components.

Typical deliverables

  • Test suite for security, bias, RAG, performance, and UI
  • Requirements-to-test traceability
  • HTML reports and release gate logic
Reusable AssetRequirementsInterview FlowPDF Output

Requirements & Quality Workspace

A combined discovery and quality workspace that turns vague ideas into reviewable requirements, traceability, test strategy, and exportable delivery artifacts.

Best suited for

Product owners, business teams, and delivery setups that need to turn unclear requests into testable, reviewable implementation artifacts faster.

Typical deliverables

  • Guided discovery flow with analysis and quality review
  • Structured requirements, traceability, and test strategy artifacts
  • Jira and PDF exports for alignment, review, and delivery kickoff
Reusable AssetClaude Code SkillsE2E TestingISTQBQA Automation

Claude Code QA Skills

Three skills for ISTQB test concepts, Playwright execution with failure analysis, and structured coverage optimization in AI-assisted delivery setups.

Best suited for

QA teams and projects that want to automate test documentation, execution, and coverage analysis as a repeatable delivery workflow.

Typical deliverables

  • ISTQB-compliant test concepts with intake and compliance checks
  • Structured E2E test execution with failure categorization
  • Coverage gap analysis with automated test creation

Capabilities & Technologies

My mix of professionally applied QA, requirements, and delivery practice plus my own AI and builder capabilities.

QA Leadership & Quality Engineering

Professionally applied QA leadership for complex delivery contexts: from test management and governance to traceability, reporting, and dependable release gates.

QA LeadershipTest ManagementRelease GatesRisk-Based TestingDefect GovernanceQuality ReportingStakeholder SteeringSecurity TestingTraceability

Test Automation & Tooling

Operational practice in automatable test design, Cucumber/Gherkin structuring, Playwright collaboration, APIs, and technical workflows for reproducible quality work.

PlaywrightCucumber / GherkinAPI TestingREST APIsTest Data DesignTool IntegrationNext.js 16TypeScripttRPC / APIsWorkflow Prototypes

Delivery & Release Governance

Release-oriented delivery with Jenkins, Bitbucket, Jira/Xray, CI/CD coordination, workflow automation, and operational hardening in day-to-day project work.

JenkinsBitbucketGitHub ActionsJira / XrayConfluenceCI/CDWorkflow AutomationReview LoopsRelease ReadinessOperational Hardening

AI & Enterprise Workflows

RAG knowledge systems, n8n automation workflows, and AI-assisted delivery processes for enterprise environments. From LightRAG and Open WebUI to MCP and agentic design.

RAG-ArchitekturMCP (Model Context Protocol)AI-Assisted RequirementsAgentic Workflowsn8nLightRAGOpen WebUIAI Agent DevelopmentPrompt EngineeringRed-Teaming

Consulting Practice

Requirements engineering, stakeholder facilitation, agile delivery, and strategic consulting practice as the foundation for dependable project work.

Requirements EngineeringSpecification FacilitationStakeholder FacilitationAgile DeliveryProduct ThinkingConsulting LeadershipStrategic Automation

How I Work

Three principles that make my QA, requirements, and AI practice distinct.

Quality as a Delivery Factor

I build test strategy, requirements review, and release criteria into delivery flows from the start – not as a final checkpoint, but as a steerable variable throughout the whole process.

Domain First, Feature Second

Before I automate or build a tool, I ask: which delivery problem gets smaller? What has to be true for this to hold in production?

AI With Context, Not Instead of It

I use AI deliberately where I know the requirements, the context, and the edge cases – as a lever for faster, testable artifacts, not as a substitute for domain knowledge.

Contact

Interested in working together? I'd love to hear from you.