Google Cloud Dataflow Expert in Germany

• Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
• Collaborated on product features, achieving measurable impact and maintaining strong client relationships.

• KPI dashboards for inventory and goods received quality control & testing ETL, dataset, dataflows and report development, complex DAX solutions

Data sources: Dataverse, RDB, Excel

Tools: ETL, data modeling, data flows, Power Query, M, Power BI, complex DAX

• Capacity: 25%

• Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
• Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
• Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
• Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
• Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
• Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
• Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
• Created and maintained dashboards in Power BI to provide actionable insights.

• Helped to design the AWS infrastructure, integrated services and backend architecture for use cases of an on-premise solution and partial migrations to AWS with fast response times
• Implemented automated AWS integration test suites
• Implemented mission-critical components and delivered them before the deadline in a production-ready state with operation and monitoring concepts
• This 2-month subproject was about building a data-intense pipeline (5 TB) to be enriched continuously with data
• Designed and implemented reusable AWS CDK constructs to be used across the company’s teams to enable faster onboarding with AWS
• Coached on AWS topics, distributed software patterns, security, domain-driven design, agile collaboration and documentation to improve performance and collaboration
• Technologies: AWS, GitHub Actions, ETL, monitoring, operations, TypeScript, Python, AWS CDK, CloudFormation, Java, Docker, AWS ECS, AWS Lambda, serverless, Jenkins, DevOps principles

• Design, preparation and delivery of a webinar on 'RAG in Practice: How publishers create real value with AI'
• Preparing technical and strategic content on Retrieval Augmented Generation (RAG) for a mixed audience from the publishing industry
• Presenting specific use cases, technical backgrounds, common challenges and solution approaches when using RAG
• Providing practical insights into data preparation, model selection and output optimization in the context of digital publishing portals
• Conceptual and technical preparation of the webinar
• Selecting and presenting practical use cases from the publishing environment
• Developing technical backgrounds for implementing RAG systems
• Presenting and explaining typical challenges and solution strategies
• Large Language Models (LLMs)
• Retrieval Augmented Generation (RAG)

• Optimizing the online shop search function based on Magento and OpenSearch
• Advising on eliminating search pain points (composite search, case-insensitive search)
• Data modeling of products (parent) and items (child)
• Workshops to convey domain-specific search understanding

• Developed KPI dashboards using Power BI and DAX for 4+ business units, improving reporting transparency and strategic decision support.
• Migrated enterprise finance data views into dbt models, implementing modular SQL transformations, version-controlled data pipelines, and automated documentation to create a scalable analytics layer.
• Built dimensional data models in Snowflake for enterprise finance data, enabling scalable forecasting and supporting executive decision-making.
• Designed end-to-end ETL/ELT pipelines using Snowflake and SAP HANA, integrating data from 3+ enterprise systems.
• Automated monthly reporting workflows using SQL and Power BI, delivering strong business impact by reducing manual effort by 80%.
• Collaborated with finance stakeholders to translate business requirements into analytical data models, supporting strategic decision-making cycles.
• Delivered ad-hoc financial reports using Power BI, reducing turnaround time by 60%.
• Implemented data validation logic in SQL, resolving 95% of recurring data quality issues.

• Architected a self-service Google Kubernetes Engine (GKE) platform for a major financial institution (Commerzbank), enabling 1000+ users across hundreds of product teams to autonomously provision resources and significantly accelerate development cycles.
• Designed a data-product-oriented platform architecture for the UK Department for Transport (DfT) to serve over 1500 direct end-users and numerous connected third-party systems, enhancing data accessibility and governance.
• Drove business growth by developing the strategic roadmap for the 'One Cloud' business line, targeting a 10% revenue increase.
• Served as a key member of the CTO Authority, providing strategic guidance on internal cloud initiatives and best practices.

• Set up and operated data-driven analysis and reporting processes in Power BI, Tableau, and SAP

• Integrated SAP FICO and Workday data into Power Platform workflows to automate HR reports

• Developed predictive ML models for workforce planning and KPI management

• Used Azure and GCP (BigQuery, Dataflow) to process large data volumes (Big Data pipelines)

• Automated reporting increased analysis efficiency by 40%

• Introduced a GCP-based analysis model for employee turnover

• 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

• Agile requirements management according to consultation guidelines
• Development of a master data system
• Preparatory activities for audits
• Managing the development team for the creation of microservices for billing preparation and execution
• Designing connections to various internal and external interfaces
• Designing release cycles and planning emergency management

• Replacement of a Java application and the Oracle DB for loading the internal WerWasWo system using SSIS.
• Development of SSIS packages to load text files into the database (SQL Server)
• Development of a database project for deployment on various servers
• Creation of queries to monitor the loading runs
• Development of a PowerShell script to automate the deployment of the SSDT projects.
• Oracle, SQL Developer, Microsoft SQL Server 2022 on-premises, SQL Server Management Studio v21, Visual Studio 2022, SSIS, SSDT, PowerShell.

• Design, architecture & development/programming of ETL/ELT data pipelines, DWH, BI solution
• Technical project lead, POC – proof-of-concept creation
• Liaison between business units and technical teams
• Azure DevOps Boards & Jira
• Data modeling & data engineering – data warehouse & data mart
• Azure (Data Factory, Azure SQL, Azure DevOps CI/CD, Azure Data Lake V2, Business Central REST API, OData API, OAuth2 tokens)
• SharePoint lists & API for ADF, Firebird DB, Postgres DB, DB2
• Power BI (Power Query), DAX, Excel PBI add-on, GIS data
• Automated ETL process monitoring/logging, performance monitoring, error monitoring – capturing & resolution
• Index performance tuning & statistics monitoring, Transact-SQL
• Data security – MFA (multi-factor authentication) & OAuth2, MS Graph, Azure networks & firewalls, gateways, roles, user groups – with read/write permissions
• Sources – Vario Bill, Camunda, Radius, Geo Database, OTRS, PAST, MS Dynamics Business Central, Azure Blob Data Lake, SharePoint lists

• Led a team of 6 software engineers to develop and maintain an AI-driven healthcare platform, enabling automated diagnostics and prescriptions based on real-time ECG data analysis
• Designed and developed a robust Revenue Cycle Management (RCM) system, integrating HL7 and FHIR APIs to enable seamless interoperability, real-time data exchange, and HIPAA-compliant data handling, improving billing efficiency, claim processing, and regulatory adherence in healthcare operations
• Migrated a legacy monolithic application to a scalable microservices architecture, enhancing system modularity, scalability and maintainability while implementing key design patterns such as Strangler, Database-per-Service, API Gateway, Saga and CQRS for efficient service communication and transaction management
• Architected and led a C# 9/.NET 6 microservices ecosystem handling hotel reservations, payments, and loyalty programs, enabling 99.99% uptime across 10+ services
• Defined OpenAPI/Swagger contracts and auto-generated client SDKs, reducing front-to-backend integration time by 50%
• Containerized each service with Docker and orchestrated deployments via Kubernetes, slashing release lead time from days to hours
• Designed PostgreSQL schemas optimized for high-volume transactional workloads and implemented Redis caching layers to accelerate read-heavy endpoints by 80%
• Built Kafka streaming pipelines for real-time availability updates and audit logs, processing 2 million+ events per hour with end-to-end delivery guarantees
• Implemented unit and integration tests for React applications using Jest and React Testing Library, ensuring 80%+ test coverage, improving component reliability, and preventing regressions
• Defined and deployed AWS cloud infrastructure using Terraform, while containerizing and orchestrating microservices with Docker and Kubernetes, improving automation and system scalability
• Built a scalable full-stack booking application using React 18 and Django REST Framework, integrating Celery and Redis for asynchronous task processing, while deploying on GCP with Cloud Run and Firestore, enabling real-time scheduling, payment processing, and automated notifications
• Mentored junior developers through code reviews, pair programming, and knowledge-sharing sessions, improving team efficiency by 30% while maintaining comprehensive API documentation using Swagger/OpenAPI