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). My direction is Data & Process Analytics → Analytics Engineering → Microsoft Data Platform, supported by practical work rather than job-title claims.

I work with SQL, Python, data modelling, APIs, data quality and reproducible analytical workflows. I am developing my skills in Power BI and Microsoft Fabric, while exploring agentic AI and process automation through bounded, testable implementations.

I am seeking a mandatory internship in Hamburg, ideally in analytics, BI, analytics engineering or a Microsoft-oriented data environment, with a perspective for continued employment.

Before retraining, I worked in B2B PR, marketing and corporate communications. I spent over two years living and working in the United States (2017–2020), primarily in New York City and Miami, with additional experience in Washington, D.C.

This international background helps me connect technical work with business requirements, stakeholder communication and cross-functional collaboration.

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:

1. Cisco Switching Lab — physical infrastructure, Cisco CLI, secure administration and IPv4/CCNA context.
2. IPv4 Subnet Calculator Multilang — one bounded domain problem implemented in three languages, with explicit validation, shared domain test cases and CI.
3. Spring Boot Process API Basics — progression from compact Java logic toward a layered HTTP API with validation and persistence.

This is supporting breadth around my analytics engineering and data-platform 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 where they add value, while independently reviewing, testing and validating their output

I use AI to support development, not to replace technical understanding. I remain responsible for the requirements, design decisions, correctness and maintainability of my work.

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