Azure Data Lake Storage Experts in Germany

• Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
• Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
• Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
• Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.

Multi-Agent Document-Generation Platform

Problem:

Turn a single prompt into a well-researched, structured and cited document, while keeping the user in control: every stage can be reviewed, corrected or revisited before the system moves on.

Architecture:

A multi-agent system orchestrated by a central controller. More than 20 specialised pydantic-ai subagents (understanding, structuring, planning, research, writing, assembly, cross-reference check, glossary, citation, reference table and more) pass results forward through a shared plan persisted in Postgres (asyncpg); a runner layer dispatches each task by name to its agent, and mechanical steps such as formatting, table of contents and list of figures are deterministic code instead of LLM calls. Provider (Anthropic or OpenAI) is a configuration setting, and responses stream over Server-Sent Events.

Hosting & Infrastructure:

Stateless container run as a Scaleway Serverless Container on a private network with managed PostgreSQL, object storage for finished documents and assets, and centralized log shipping (Loki / Scaleway Cockpit), provisioned with OpenTofu modules and deployed through GitLab CI; usage is metered per API key.

My Contribution & Key Decisions:

• Central decision controller: a satisfaction classifier turns the user's free-text feedback into a verdict (keep working, advance, cancel or revise an earlier task), and a middleware applies it to the plan. Plan progression supports jumping back to earlier tasks and re-running the stages after them.
• Research and writing fan-out: planning produces a plan from which a research-design agent generates individual research tasks and a writing-design agent generates individual writing tasks, each linked to the research it needs. Every task runs in its own dedicated agent with its own conversation history.
• Self-review and human-in-the-loop: after each step a critical-thinking agent reviews the output; the user can accept, refine, or ask questions about a critique before the plan advances.
• MCP integrations: two remote MCP servers, web search and internal document search, connected through pydantic-ai with retries and long-running tool-call timeouts. Classifier agents decide per task whether web search is needed, and a small model condenses raw search results into structured findings before they reach the writing agents.

Usage Metering:

Clients authenticate with an API key validated against a key-management service; consumption is charged in credits calculated from token usage per model plus a markup, and exhausted quota is answered with HTTP 429.

• Advising companies on building, migrating and optimising BI and reporting landscapes (Power BI, SQL, Python, ETL)

• 5 client engagements in real estate and finance since 05/2025: taking over and stabilising existing reporting, automating recurring standard and management reports, building cash-flow models

• Proposal and feasibility assessments for BI and reporting projects

• Using AI-assisted development (Claude Code) to accelerate automation, tooling and web/app development

• Custom ERP system

• Problem: A client's core processes ran on scattered, siloed Excel files with no central data storage – error-prone, hard to scale and impossible to analyse end-to-end.

• Approach: Captured the business processes and requirements, modelled the data and developed iteratively together with the business team.

• Implementation: Built a tailored, web-based ERP system with a central database, role-based modules and automated reporting – delivered using AI-assisted development in Claude Code.

• Timesheet app

• Starting point: Time tracking based on an overgrown, macro-heavy Excel template – maintenance-intensive, single-user and error-prone.

• Implementation: Migrated all functionality and VBA macros into a standalone web app with central data storage, multi-user support and automated reporting.

• Cash-flow modelling

• Starting point: The existing cash-flow model covered standing investments only; project developments were missing from steering.

• Implementation: Built and extended the CF model to include project-development cash flows.

• Optimisation: Reviewed and optimised existing CF models and expanded the KPI outputs for reporting and steering.

Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.

Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows

Clients: CARIAD, AUDI AG

• Built and deployed enterprise MLOps pipelines using Azure Machine Learning and Databricks. Reduced model release cycles by 95 percent, from four weeks to two days, through automated CI CD workflows.
• Delivered cloud native DevOps platforms for ADAS programs using Azure data services, Kubernetes, and Terraform based infrastructure provisioning.
• Designed high availability architectures with automated failover. Cut system downtime by 85 percent for mission critical energy trading platforms.
• Developed and integrated AI agents and enterprise chatbots using LangChain, AutoGPT, and GPT models. Enabled autonomous workflows and decision driven automation.
• Reduced cloud infrastructure spend by 40 percent through autoscaling strategies, spot instance usage, and policy driven resource governance across Azure and AWS.
• Partnered with Data Scientists, ML Engineers, Product Managers, and executive stakeholders to deliver large scale automotive and energy solutions.
• Implemented GitOps driven CI CD pipelines supporting automotive software delivery for over 500 engineers across distributed product teams.
• Designed and operated Kubernetes platforms on Azure AKS. Improved deployment stability and reduced rollback events by 70 percent.
• Implemented observability and monitoring stacks using Prometheus, Grafana, and Azure Monitor. Achieved 99.9 percent service availability targets.

Responsibilities: Design and operation of end-to-end cloud data platforms for enterprise clients in publishing and finance, including infrastructure automation, pipeline development, monitoring, and data quality.

Activities:

• Built multi-layer data architectures on Databricks (Apache Spark, Delta Lake), BigQuery, and GCP
• Fully automated cloud infrastructure with Terraform across 3 environments (DEV/STG/PRD)
• Developed automated data pipelines with Python, dbt, and GCP services for various data sources
• Built monitoring and alerting systems for real-time platform monitoring
• Implemented data versioning and quality checks at every layer
• Designed automated testing and deployment pipelines in GitLab and Bitbucket

Achievements:

• Doubled production data processing capacity, reduced spike response time from minutes to ≤15 s, server errors ≈ 0
• Replaced 3,000 lines of manual configuration with a reusable automation module for 7 customer domains, reducing configuration errors to 0
• Delivered a complete end-to-end data platform at ~10 €/month in infrastructure costs
• Migrated 7 database tables with 0 downstream issues
• Removed 100 % of exposed credentials, eliminating dependency on external vendors
• Delivered 3-team integration in 1 sprint

• Led strategic software development of fusion algorithms for precise object tracking, trajectory prediction, and environment modeling based on multimodal sensor data (e.g., camera, LiDAR, radar, GNSS, IMU)
• Developed and implemented navigation algorithms for autonomous vehicles, including path planning, obstacle avoidance, and sensor fusion of visual, inertial, and distance-based sensor sources
• Automated extraction and training processes with CI/CD
• Developed and optimized data pipelines and processes in Microsoft Azure using Apache Spark, Databricks, and PySpark
• Developed and optimized embedded software for automotive control units
• Designed latency-critical software for real-time control in robotic systems with RTOS (freeRTOS, SAFERTOS)
• Used the Vector toolchain (CANdela, DaVinci, CANoe) for configuration and diagnostics
• Optimized existing data pipelines and processes (ETL, data warehouse, SQL)
• Developed and trained machine learning models using PyTorch
• Created deep-learning-based object detection and visual SLAM algorithms, trained on combined data from camera, LiDAR, and IMU sensors
• Implemented computer vision algorithms for object detection and classification in robotic systems using OpenCV and YOLO, utilizing synchronized image and depth data
• Implemented behavior-based control systems for autonomous robots using ROS2 Behavior Trees
• Performed testing, release, and integration of sensor fusion algorithms into automotive production programs
• Ensured adherence to proper software development processes and safety standards to guarantee high data quality (MISRA, ISO 26262, ASPICE)

• Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
• Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
• Storage of the silver layer on Hadoop and the gold layer in Oracle.
• Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
• Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
• Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
• Versioning changes in GitHub and deployment via the CI/CD portal.
• Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
• Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
• Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
• Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
• Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.

• Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
• Independently designing analytics solutions with Python, SQL, etc.
• Designing and implementing ETLs and data pipelines
• Creating and maintaining APIs
• Independently applying CI/CD, testing, and version control
• Data modeling
• Model development and optimization
• Anomaly detection with AI
• Predictive analytics

Used technologies:

• Snowflake
• Fabric
• Azure Synapse Analytics
• Azure DataFactory
• Azure Data Lake
• Azure DevOps
• Databricks
• Spark
• CI/CD
• SQL Database
• Python
• Power Platform

• Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)

• Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)

• Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)

• Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)

• Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)

• Maintenance and enhancement of a Siebel CRM Service & Marketing implementation (Siebel 23.1, OpenText, OBIEE, Informatica).

• Design and provide best practices on data modeling for dbt, including changing dimensions, late arriving data handling, and testing
• Design the ingestion flow from other systems into S3 and Redshift
• Design and implement new partitions for Dagster and incremental loading with dbt
• Map business requirements to technical architectures
• Instruct junior team members

• Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
• Performance Optimization of Data Ingestion of ETL Pipeline
• Development of Data Validation using Great Expectations
• Leading of the data migration for two sources exchanges
• Data Modeling in AWS Redshift

MLOps

• Model inference implementation by mlflow and AWS SageMaker
• Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
• Implementatino of Model Registry and artifactory using mlflow
• Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
• Feature importance using mlflow

Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy

Projects:

Project: RWE – Development of a company-wide Zero Trust cybersecurity architecture (CITADEL) Role: Senior Enterprise Cybersecurity Architect / Zero Trust Architect Company: RWE AG Description: Concept and implementation of the strategic CITADEL cybersecurity target architecture at RWE, based on the Zero Trust architecture principle and aligned with regulatory requirements such as NIS2, ISO 27001 and company-wide security governance policies. The goal was to build a measurable, auditable and scalable security architecture with a strong focus on Identity Governance, compliance transparency and operational manageability. Responsibilities & Achievements:

• Zero Trust architecture design: Developed a company-wide Zero Trust reference architecture (Identity, Device, Network, Application, Data) including trust zones, control points and enforcement mechanisms according to NIS2.
• Identity & Access Governance (IGA): Designed and introduced IGA governance structures including role models, recertification processes, segregation of duties (SoD) and lifecycle management for identities and access.
• Security governance & KPIs: Defined and implemented security KPIs and metrics to manage Zero Trust maturity, identity risks and compliance at the management level.
• Compliance & reporting: Built standardized compliance reports and dashboards to support internal audits, external assessments and regulatory evidence (e.g. NIS2).
• Architecture & stakeholder alignment: Worked closely with Enterprise Architecture, IT operations and business units to integrate the CITADEL architecture into existing IT and security landscapes.
• Strategic security consulting: Advised programs and projects on Zero Trust compliance, identity centricity and regulatory requirements in the energy and critical infrastructure (KRITIS) environment. Technologies & Methods: Zero Trust Architecture, NIS2, Identity Governance & Administration (IGA), IAM, RBAC, SoD, Entra ID, SailPoint, Zscaler, Terraform / IaC, Policy as Code, security KPIs, compliance reporting, NIST 2.0, ISO 27001, Enterprise Security Architecture, governance frameworks, risk & control management

Project: Scalable AI Workbench Platform on Microsoft Azure Role: Cloud Architect & Engineer Company: Siemens Energy Description: Design, development and operation of a secure, modular cloud infrastructure to support Data Science, Machine Learning and AI applications for various engineering teams at Siemens Energy. Responsibilities & Achievements:

• Cloud architecture: Designed and implemented an Infrastructure-as-Code solution (Terraform) for automated provisioning of Azure resources (Resource Groups, Storage Accounts, Cosmos DB, Application Insights, networking, PostgreSQL Flexible Server, Azure Container Apps, Azure Container Registry).
• Developer portal: Used Backstage with custom frontend and backend plugins (Node.js, TypeScript, React.js, PostgreSQL, Container Apps) to enable self-service and empower developers, data scientists and AI/ML engineers.
• Role-based access control: Implemented Azure RBAC to grant targeted access (e.g. Storage Blob Data Contributor, Reader) to engineering groups (e.g. AI Engineers) for relevant resources.
• Data platform engineering: Built and configured a multi-layered storage landscape (Raw, Curated, Vector data), including automated container creation and access control for advanced analytics and AI workloads.
• DevOps integration: Integrated with Azure DevOps for CI/CD pipelines to automate deployment, monitoring and compliance.
• Security & compliance: Implemented Private Endpoints, network policies and Managed Identities to ensure data protection and regulatory compliance.
• Collaboration: Worked closely with cross-functional teams to align the cloud infrastructure with business and technical requirements and drive digital transformation at Siemens Energy. Technologies: Azure, Terraform, Azure DevOps, Cosmos DB, Application Insights, Azure Storage, Private Endpoints, Azure Synapse, Azure Machine Learning, Azure Entra ID, RBAC, Backstage, Node.js, React.js, PostgreSQL, Python (automation), Git