Data Pipeline Experts in Hamburg

• Designed and implemented a Python-based ETL pipeline with the Dagster framework to transform raw energy data from heterogeneous sources using InfluxDB and visualizations in Grafana
• Defined time-based and dependency-based jobs
• Deployed to managed Kubernetes clusters using Helm
• Integrated InfluxDB Cloud
• Prepared data for use in Grafana, including cleaning, normalization, and time-based resampling in Python
• Developed dashboards and visualizations in Grafana
• Developed unit tests with mocking using pytest
• Set up a CI/CD pipeline in GitLab

Technologies: Python, Dagster, InfluxDB, Grafana, pandas, pytest, REST, CI/CD, GitLab, Container, Kubernetes, Helm, Docker, Cloud

• Implementing cross-industry applications with LLMs
• Developing cloud infrastructure for clients
• Implemented end-to-end data pipeline to deploy models in real time
• Managed overall IT system administration and desktop support

Self-directed exploration of agentic AI development methods, taken from idea to a working, publicly usable application

• Built an interactive planning tool for modelling retirement withdrawals and tax strategy using an agentic AI (vibe coding) development approach – demonstrating self-directed investigation of new AI-assisted development methods
• Delivered live, tax-aware spending projections and adjustable user inputs; shipped as a free, install-free browser application built in Python, with attention to usability for non-technical users

• Development of ETL pipelines with Talend (7/ Enterprise)
• Development/adaptation of database schema/database functions (PostgreSQL)
• Development of GIT CI pipelines

Technologies: Talend Enterprise, Git, PostgreSQL, Dbeaver, SQL, PL-SQL, Liquibase

• Successfully completed a 3.5 month data science course, earning the ‘Certified Data Scientist’ title from XDi, Germany (AZAV certified).
• Covered supervised and unsupervised machine learning algorithms.
• Covered natural language processing using Python.

• Planning, steering, and monitoring the Corporate Responsibility Tech Enablement project for IT
• Concept and implementation of the EUDR (EU Deforestation Regulation)
• Concept and implementation of the Eco-Design Regulation
• Continued support of projects within the Corporate Responsibility strategy
• Definition and review of guidelines (e.g. governance, success metrics) and, if needed, analysis of issues and their solutions
• Overall project management, marketing, communication, and quality assurance for the projects
• Creation of the project plan including timeline/roadmap, budget, and resources
• Ensuring compliance with budget and deadlines
• Defining guidelines for project control, methods, and priorities
• Planning and tracking resources and effort, also together with other IT teams
• Evaluation of projects and preparation of results (reporting to management)
• Identification and reduction of risks
• Regular communication with stakeholders
• Steering internal and external stakeholders and service providers (Jira, Confluence, backlog management, task tracking, SharePoint, MS 365 platform etc.)
• Development of standards and process automation

Label: Azure Cloud, Jira, Confluence, Power BI, SAP BW, AWS, Miro, Bee360, Draw.io, SharePoint, MS 365 platform, Trello etc.

• Architected and deployed LLM based AI agents, RAG pipelines, and vector search solutions for decision support across retail banking department.
• Developed and shipped robust AI pipelines with guardrails, error handling, monitoring, and fallback logic ensuring high reliability outcomes and compliance with data privacy.
• Developed and deployed ML models to identify transactional anomalies, improving fraud detection and risk assessment in high-volume datasets for credit risk modelling.
• Built, evaluated and fine-tuned ML models to generate propensity scores for customers used to drive personalized targeting campaigns for credit cards and personal finance/loan products.
• Developed an NLP pipeline using BERT embeddings and spaCy NER for SMS/email analysis and customer query logs.
• Trained machine learning models using Isolation Forest to classify user behaviour and detect anomalies.
• Extracted, cleaned, enriched and feature engineered datasets from different sources to build feature stores that powered ML model training.
• Led development of dashboards using Power BI, Grafana, and Prometheus to monitor model performances, KPI trends, and marketing metrics.
• Built multi-touch attribution models using logistic regression and time-decay weights to evaluate lead quality.
• Developed scalable ETL pipelines from CRM, T24, SAP, and ERP, supporting millions of monthly transactions.
• Integrated testing and CI/CD workflows for robust data pipeline deployment.

• Developing Kotlin/Spring Boot microservices deployed on Kubernetes and AWS Lambda
• Implementing monitoring solutions
• Backend development for Traigo platform (booking/analysis for freight wagons)
• Determining wagon stays in geofences
• Routing of position data
• Forwarding position data and geofence events to clients using ITSS
• Developing and deploying services in both Kubernetes and AWS Lambda environments
• Tech stack: Kotlin, Spring Boot, Kubernetes, AWS Lambda, PostgreSQL, DynamoDB, Protocol Buffers

• Optimized and introduced Salesforce processes (Sales Cloud + Marketing Cloud) to support sales.
• Implemented processes for collecting requirements from various departments.
• Expanded reporting for transparency of project progress and backlog priorities.
• Managed the backlog, created user stories and acceptance criteria.

• Build data pipelines for raw and curated data layers using AWS S3, Glue, Athena, and Lake Formation
• Establish CI/CD using GitHub Actions or GitLab CI with CodePipeline
• Prototype models into demo APIs packaged with Docker, versioned with Git, added basic tests with pytest, and assist deployments on AWS SageMaker Endpoint
• Perform exploratory data analysis and feature engineering with pandas and PySpark; track experiments in MLflow or Weights and Biases
• Design and execute A/B tests to optimize user engagement and drive data-informed decisions