•  Implement microservice to automate transport and transformation of data from the factory floor to the Siemens Data Cloud, with the help of Kafka and Snowflake
• Implement streaming pipeline to validate and transform data from diverse sources, like S3 bucket or SFTP server, and move them to Snowflake and Postgres
• Implement microservices to automate live replication of data between MSSQL and Snowflake, with the help of Kafka, Apache Flink and S3 buckets
• Deploy microservices on AWS with Kubernetes and Helm charts
• Requirements engineering
• Developing data transformation applications using streaming with Kafka and Kubernetes
• Enhancing and optimizing Flink application deployments on Kubernetes using flink SQL transformations for dynamic view materialization.
• Setting up automated ETL pipeline with Rust services, SQL on RisingWave to stream data from MSSQL Server to Snowflake for 720 view definition in a large application context
• Translating T-SQL definitions to RW-SQL definitions and Snowflake SQL definitions
• Monitoring and maintaining a RisingWave database cluster in a Production Environment

? Created a data pipeline that automatically triggers data transformation and ingestion into an Iceberg table upon S3 upload, using AWS Lambda for seamless operation.

? Enabled querying of aggregated data from the Iceberg table to serve as input for a prediction model to support data-driven decision-making processes.

? Create an SqlMesh project for an incrementally updated data warehouse

? Build a reproducible CICD pipeline for Databricks jobs
? Introduce a IaC for setting up infrastructure in AWS and Databricks with terraform 

? Developed a custom AI platform for Henkel employees, implementing a microservices architecture that integrates AI functionalities in Python with a front-end built using TypeScript and Angular.

? Deployed the entire application ecosystem on Azure Cloud using Kubernetes.

? Established Azure CI/CD pipelines to automate testing, build Docker images, and push them to a secure image registry.

? Leveraged ArgoCD within Kubernetes for continuous, automated deployment of microservices.

? Employed Infrastructure as Code (IaC) best practices with Terraform for provisioning and managing a Kubernetes cluster. Established Azure CI/CD pipelines for it as well.
? Deployed an App-of-Apps pattern using Helm charts in ArgoCD. 

• Implement microservices to enable bidirectional data synchronization between Salesforce and IRIS, automating data integration processes
• Implement a microservice to automate synchronisation between IRIS and ElasticSearch
• Implement a scalable Salesforce Connector service, for consuming messages from Salesforce topics via the Salesforce pub/sub API, and forwarding them to Kafka topics after format validation. Designed for easy deployment and scalability, the connector supports multiple instances for different Salesforce topics.
• Deploy services on Azure with Kubernetes and Helm charts
• Requirements engineering

• Implement a microservice that aggregates payment data, transforms it into Elasticsearch documents, and indexes it for improved data accessibility and search functionality
• Implement a microservices system, with ingest and query-API components, for streaming Kafka data to Postgres and providing HTTP access
• Deploy services on AWS ECS with Docker and Terraform
• Requirements engineering

• Implement a microservices tool providing a dashboard overview of AWS expenses, offering custom recommendations for cost optimization, especially for AWS Logs, EC2 and ECS services.
• Deploy services on AWS with CDK and using AWS Lambda

• Backend web application: development of an e-payment API according to a new European law
• Deploy services with Docker
• Implementation of REST APIs for data access
• Requirements engineering

• Test engineer for terminal payment software
• Implementation and maintenance of automated functional tests for payment terminals
• Test driven development and continuous integration and deployment pipelines
• Requirements engineering