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IHK retraining program in Data and Process Analysis · Data/BI Analyst track · SQL · Power BI · Python · Microsoft Fabric/Azure · Hamburg
Portfolio overview · Personal website · LinkedIn
I am currently retraining as a Fachinformatiker für Daten- und Prozessanalyse (IHK, expected completion 06/2027) and building a practical Data/BI portfolio around SQL, Power BI, Python, data quality, process analysis and the Microsoft Data Stack.
Currently seeking a mandatory internship in Hamburg beginning on 9 September 2026, ideally in Data/BI, process analysis, reporting or a Microsoft-oriented data environment.
My target direction is Data/BI Analyst with a strong interest in Power BI, Microsoft Fabric, Azure fundamentals, relational data modelling, reporting workflows and process-oriented analytics.
My previous background in PR, communication, international business contexts, documentation and project-oriented stakeholder work helps me connect technical data work with business needs, decision-making and clear communication.
| Area | Practical focus |
|---|---|
| Data & BI | Power BI, dashboards, KPIs, reporting logic, business questions |
| SQL & Data Modelling | Relational databases, clean table structures, joins, aggregation, data quality |
| Python Data Workflows | pandas, APIs, CSV/JSON, data quality, KPI reconciliation and reproducible reporting |
| Microsoft Data Stack | Power BI, Microsoft SQL Server, Microsoft Fabric perspective, Azure fundamentals |
| Process Analysis & Mining | Event logs, process KPIs, process discovery, bottleneck analysis and process improvement |
| Business Communication | Translating technical findings into clear explanations for stakeholders |
| IT Foundations | Linux fundamentals, Cisco/CCNA-oriented networking, Java and C++, API basics, automated testing and CI |
| Notebook | Platform | Focus |
|---|---|---|
| Delivery Delay KPI Analysis / GitHub mirror | Kaggle / GitHub | Public Python/pandas EDA with business-rule validation, data-quality checks and BI-style KPI aggregation. Kaggle login may be required for the Kaggle-rendered version. |
| Project | What it demonstrates |
|---|---|
| Fabric Service Operations Analytics | Tested local pipeline on deterministic synthetic service-operations data: Bronze/Silver/Gold Parquet layers, DuckDB SQL marts, metric contracts and SLA breach analysis. Microsoft Fabric and Power BI mapping planned, not implemented |
| Service Operations AI Orchestration | Java 21 / Spring Boot / Spring AI companion that exposes the pinned analytics snapshot through two read-only tools, keeping KPI definitions and interpretation boundaries outside the model. Deterministic checks implemented, live-provider evaluation prepared but not yet executed |
| Excel to SQL Data Workflows | Cross-tool comparison of equivalent analytical intent across Excel, Power Query M, SQL Server T-SQL and Power BI DAX, backed by deterministic data, a generated workbook, 19 tests and Windows/Linux CI |
| SQL Server Docker Basics | Reproducible SQL Server analytics lab with relational integrity, star schema and end-to-end CI |
| Python Data Basics | Tested Python workflow for CSV validation, auditable rejections, KPI reconciliation and reporting |
| Network Operations Data Lab | Public-safe operations analytics over a sanitized Cisco baseline using Python, SQLite, SQL validation views, automated tests and BI-style reporting |
| Music Production Data Lab | Public-safe relational analytics model with SQLite, reproducible Python builds, SQL reporting views, automated data-quality checks and documented Power BI semantic modelling with DAX evidence |
| Flask Country Data API | Reproducible World Bank data service with validated ingestion, constrained SQLite persistence, versioned Flask endpoints, SQL data-quality checks, OpenAPI 3.1 and cross-platform CI |
| Hamburg District Data Basics | Tested Altona district-data workflow using official Hamburg data, pandas, dataset contracts, automated validation, descriptive analysis and Power BI design |
| Project | What it demonstrates |
|---|---|
| Cisco Switching Lab | Physical Catalyst 3560CX lab documenting secure management, IOS maintenance, NTP, verification and CCNA-oriented switching work |
| IPv4 Subnet Calculator Multilang | One IPv4/CIDR specification implemented in Java 21, C++20 and Python 3.12 with shared contract cases and GitHub Actions |
| Spring Boot Process API Basics | Small Java 21 / Spring Boot REST API with layered structure, validation and basic persistence for process-check data |
| remote-access-network-lab | Public-safe Tailscale remote-access lab documenting native SSH over a private mesh VPN, trust boundaries, least-privilege design, device lifecycle and offboarding |
These repositories are intentionally focused and practical. They are not meant to simulate enterprise-scale systems, but to document a clear learning path, reproducible workflows and steadily growing professional competence.
- The Dashboard Wasn't the Hard Part: Why KPI Definitions Matter — a project-based article on KPI semantics, denominator choices, aggregation, data quality and validation, based on Fabric Service Operations Analytics.
- Hiring Is a Prediction Problem: What Credential Inflation Changes — an essay on hiring as a prediction problem and why credential inflation can reduce differentiation while gatekeeping and informational value remain.
Three supporting projects form a deliberate progression:
- Cisco Switching Lab — physical infrastructure, Cisco CLI, secure administration and IPv4/CCNA context.
- IPv4 Subnet Calculator Multilang — one bounded domain problem implemented in three languages, with explicit validation, shared domain test cases and CI.
- Spring Boot Process API Basics — progression from compact Java logic toward a layered HTTP API with validation and persistence.
This is supporting breadth around the main Data/BI direction, not a claim of separate network-engineering or backend specialization.
I approach data work from the business or process problem first:
- understand the business process and decision context
- define what should be measured
- identify, collect and validate the required data
- model and transform data with SQL and Python
- build reproducible analytical workflows
- translate results into KPIs, reports and decision-oriented insights
- communicate assumptions, limitations and recommendations clearly
- use AI-assisted tools responsibly while retaining ownership of the result
| Area | Credential / Focus | Status |
|---|---|---|
| Data & Process Analysis | Fachinformatiker für Daten- und Prozessanalyse (IHK) | In retraining, completion 06/2027 |
| Cloud Fundamentals | Microsoft Azure Fundamentals (AZ-900) | Exam scheduled 2 September 2026 |
| Data Fundamentals | Microsoft Azure Data Fundamentals (DP-900) | Exam scheduled 12/2026 |
| Business Intelligence | Microsoft Power BI Data Analyst (PL-300) | Exam planned 2027 |
| Office Productivity | Microsoft Office Specialist: Excel Associate – Microsoft 365 Apps (MO-210) | Exam planned 2027 |
| Networking | Cisco CCNA (200-301) | Exam scheduled 20 October 2026 |
| Python | PCEP – Certified Entry-Level Python Programmer | Exam planned 09/2026 |
| Python | PCAP – Certified Associate Python Programmer | Exam planned 10/2026 |
| Linux | Linux Essentials | Completed |
| Agile / Scrum | EXIN Agile Scrum Master | Completed |
| Product Ownership | EXIN Agile Product Owner Bridge | Completed |
| Instructor Qualification | AEVO / AdA – Ausbildereignungsprüfung (IHK / Hamburg Chamber of Commerce) | Exam planned 07/2027 |
| Communication | IHK certificate in PR- und Medienarbeit | Completed |
I am currently strengthening this portfolio toward:
- SQL-based data modelling and reporting queries
- Power BI dashboards with clear KPIs and clean data models
- Python-based data collection, cleaning and exploratory analysis
- Microsoft Fabric and Azure fundamentals as the next platform layer
- process-oriented analytics for real business questions
- clear documentation for recruiters, internship providers and technical reviewers
- stronger links between technical implementation, business interpretation and communication
- Portfolio overview: datatidehh.github.io/DataTideHH
- Website: datatidehh.de
- LinkedIn: linkedin.com/in/datatidehh
- Kaggle: kaggle.com/datatidehh
- GitHub: github.com/DataTideHH
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