Data & Process Analytics → Analytics Engineering · Agentic AI & Automation (focus) · SQL · Python · Power BI · Microsoft Fabric · Hamburg
Portfolio overview · Personal website · LinkedIn
I am retraining as a Fachinformatiker für Daten- und Prozessanalyse (IHK, planned completion 12/2027), focusing on Analytics Engineering, Agentic AI & Automation, and the Microsoft Data Platform.
My primary interest is in connecting reliable data engineering with intelligent business process automation — turning data into actionable insights and building AI-assisted workflows that are transparent, testable and maintainable.
I work with SQL, Python, data modelling, APIs, data quality and reproducible analytical pipelines. My portfolio includes hands-on projects in data engineering and process analytics, alongside a Java / Spring AI project implementing governed tool calling, validated analytics data and deterministic testing. I am continuing to develop my skills in Power BI, Microsoft Fabric and AI integration.
I am currently seeking a mandatory internship in Hamburg as part of my IHK retraining, with a flexible start date. I am particularly interested in data engineering, business intelligence, process intelligence and AI-driven automation. My goal is to contribute to meaningful projects, gain practical industry experience and ideally develop a long-term professional relationship.
My background combines university studies in German Studies, American Studies, Journalism and Media & Communication Studies with extensive professional experience in B2B communications, corporate PR, marketing and project work in Germany and the United States.
From 2017 to 2020, I spent more than two years living and working professionally in the United States, primarily in New York City and Miami. I also hold U.S. permanent resident status (Green Card).
This combination of technical training, business experience and international perspective helps me understand requirements, communicate across disciplines and bridge the gap between technology, business processes and the people who rely on them.
| Area | Practical focus |
|---|---|
| Analytics Engineering | SQL and Python transformations, relational data modelling, data contracts, reproducible pipelines and automated data-quality checks |
| Agentic AI & Automation | Java / Spring AI, governed tool calling, schema validation, deterministic testing and evaluation workflows |
| Data & Process Intelligence | Business KPIs, process performance, SLA analysis, bottleneck identification and evidence-based decision support |
| Microsoft Data Platform | SQL Server and developing expertise in Power BI, semantic modelling, Microsoft Fabric and Azure |
| Python & API Integration | pandas, REST APIs, data ingestion, validation and reproducible analytical workflows |
| Business & Stakeholder Communication | Translating business requirements into technical solutions, communicating across teams and connecting analytics with business value |
| IT Foundations | Linux, Cisco networking fundamentals, Java, automated testing, Git and CI/CD |
| Notebook | Platform | Focus |
|---|---|---|
| Delivery Delay KPI Analysis / GitHub mirror | Kaggle / GitHub | Python/pandas analysis of delivery performance, including KPI calculations, business-rule checks and data-quality validation. The notebook is also available on GitHub without a Kaggle login. |
The first two projects form a connected workflow: a validated analytics pipeline provides the data foundation for a controlled Spring AI tool layer.
| Project | What it demonstrates |
|---|---|
| Service Operations AI Orchestration | Java 21 / Spring AI application exposing SLA metrics from a validated, versioned analytics snapshot through two read-only tools. Includes schema validation, numerical reconciliation and a reproducible evaluation framework. Live LLM evaluation remains pending. |
| Fabric Service Operations Analytics | Local Python/SQL pipeline processing synthetic service-operations data through Bronze/Silver/Gold Parquet layers and DuckDB. Includes SLA reporting, metric contracts and derived process-event analysis. Microsoft Fabric deployment and Power BI reporting remain planned. |
| Excel to SQL Data Workflows | Explores how the same business questions can be answered in Excel, Power Query M, SQL Server T-SQL and Power BI DAX, including differences in calculation semantics and 19 automated tests. |
| Music Production Data Lab | Relational model of music-production equipment and workflows, with Python/SQLite processing, SQL reporting and documented Power BI analysis, including DAX measures and a reviewed overview export. |
| Flask Country Data API | World Bank data ingestion into SQLite, with source validation, transactional updates, versioned Flask endpoints, OpenAPI documentation and SQL-based data-quality checks. |
| SQL Server Docker Basics | SQL Server lab covering relational constraints, star-schema modelling and automated verification in CI. |
| Hamburg District Data Basics | Analysis of official Hamburg district data using pandas, documented dataset rules and validation checks, with a proposed Power BI reporting design. |
| Network Operations Data Lab | Turns sanitized Cisco infrastructure records into a SQLite data model, SQL analysis views and quality reports. Demonstrates how operational IT data can support structured reporting. |
| Python Data Basics | CSV processing workflow with input validation, traceable rejected records, KPI calculations and reporting tests. |
| Project | What it demonstrates |
|---|---|
| Cisco Switching Lab | Hands-on configuration and verification of a physical Cisco Catalyst 3560CX switch, including secure management, IOS maintenance, NTP and CCNA-related networking tasks. |
| IPv4 Subnet Calculator Multilang | The same IPv4/CIDR calculation logic implemented in Java 21, C++20 and Python 3.12, using shared test cases to compare behaviour across languages. |
| Spring Boot Process API Basics | Small Java 21 / Spring Boot REST API demonstrating request validation, layered application design and basic data persistence. |
| Remote Access Network Lab | Tailscale and SSH lab documenting private remote access, trust boundaries, least-privilege access and device offboarding. |
Each repository documents its implementation, tests and current scope.
- 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.
I start with the business or operational question, then work backwards to the data, transformations and checks needed to answer it reliably.
- Understand the problem: Identify the business process, the decision to be supported and the requirements behind it.
- Define the metrics: Establish KPI definitions, data sources, assumptions and validation rules before building reports or automation.
- Build and validate: Use SQL, Python and APIs to transform data, reconcile results and handle data-quality issues.
- Apply AI where it adds value: Connect AI-assisted workflows to validated data through controlled tool access, explicit contracts and defined interpretation boundaries.
- Test and document: Use automated tests, Git and CI to make implementations reproducible and reviewable.
- Communicate the findings: Explain results, limitations and trade-offs in terms that technical and business stakeholders can understand.
AI-assisted development tools are part of my workflow. I review their output against source data, documented requirements and automated tests.
| Area | Credential / Focus | Status |
|---|---|---|
| Data & Process Analysis | Fachinformatiker für Daten- und Prozessanalyse (IHK) | In retraining, planned completion 12/2027 |
| Cloud Fundamentals | Microsoft Azure Fundamentals (AZ-900) | Exam scheduled 30 December 2026 |
| Data Fundamentals | Microsoft Azure Data Fundamentals (DP-900) | Exam planned 02/2027 |
| 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 27 January 2027 |
| Python | PCEP – Certified Entry-Level Python Programmer | Exam planned 12/2026 |
| Python | PCAP – Certified Associate Python Programmer | Exam planned 01/2027 |
| 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 |
My next priorities are:
- solid SQL models, transformations, data contracts and automated quality checks
- a documented Power BI semantic model and report layer with unambiguous KPIs
- reproducible Python pipelines, APIs and process-oriented analytics
- practical Microsoft Fabric and Azure experience built on validated fundamentals
- small, explainable agentic AI and automation workflows that keep business logic outside the model
- documentation that makes implementation and limitations clear to employers and technical reviewers
- Portfolio overview: datatidehh.github.io/DataTideHH
- Website: datatidehh.de
- LinkedIn: linkedin.com/in/datatidehh
- Kaggle: kaggle.com/datatidehh
- GitHub: github.com/DataTideHH