Apache Derby Experts in Germany

Testing adjustments to customer requests for warehouse / logistics software

Windows 10 / 11 Atlassian Tool Chain Agile testing / Kanban

• Overall responsibility for the project lifecycle from scope definition to completion
• Close collaboration with platform teams, IT leaders, and external service providers
• Application of SAFe principles and structured sprint work
• Creation of a migration roadmap with clear milestones
• Monitoring of the lifecycle: onboarding, repository migration, replication of permissions, and system tests
• Visualization of the architecture with PlantUML and Gliffy as well as documentation in Confluence
• Regular status reports and running knowledge transfer sessions

• The goal of BIENE is to provide a uniform program for tax collection for all states.

• In tax collection, the aim is to collect the assessed taxes. This includes handling due dates, documenting incoming and outgoing payments, triggering reminders or refunds. Statute of limitations and payment reminders also play an important role. All payment transactions with banks and accounting are mapped in BIENE.

• Setting up the architecture and coordinating the provisioning of development and test environments at the Hanover location

• Installing and configuring environments on Linux servers (Apache Kafka, PostgreSQL)

• Interface tasks: coordinating and aligning the integration of software products from other departments and their test data

• Upgrading application server, JDK, Maven project structure

• Environment coordination and build management

• Implementing external interfaces

• Implementing business requirements

• Designing and implementing RESTful APIs and OpenAPI specifications

• Designing and implementing microservice architecture

• Setting up and maintaining CI/CD pipelines

• Deploying applications on OpenShift

• Creating technical documentation and diagrams

• Working with SQL databases (Oracle and PostgreSQL)

• Setting up authentication and authorization for the application and users

• Containerizing the application (automated deployment via CI/CD pipeline)

• Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).

• Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.

• Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.

• CMP also includes the complete hardware management lifecycle.

• Kubernetes, OpenStack and Hadoop are used as the foundation.

• The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.

• Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.

• Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.

• OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.

• Development of a Java application Rudi: SOAP, REST, containers, DB.

• Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).

• Tribe Home, Product Area 4 - Customer in Life, Squad Cybertron.
• Analysis, design, development, and testing of new requirements for Optimmo, mortgage financing software.
• Analysis, design, development, and testing of the JEE application MWS kredit-baufi.
• Analysis, design, development, and testing of the Wicket Optimmo application.
• Analysis, design, development, and testing of kredit-baufi batch programs.
• Migration of kredit-baufi batches to RHEL9.
• CiL SCS consumer pacts with Finagle.
• CiL SCS touch point architecture integration.
• Team size: 10.
• Tools / Frameworks: OpenJDK Java 17, Kotlin 1.6.0, Azure, Jenkins, Stash, JEE, GitLab, JIRA, Confluence, git, Spring 6.0, Spring Cloud, Spring Data, Hibernate, JPA, JMS, Kafka, Oracle, PL/SQL, Red Hat Enterprise Linux (RHEL), Maven, REST, JBoss, IntelliJ IDEA, Wicket 9 and 10, Istio.

• Platform: Python ecosystem, RHEL 8, K10, AWS IoT Core, AWS Lambda, MLOps
• Software: Java JEE/cloud, IntelliJ IDEA, AWS IoT Core, AWS Edge and Lambda, AWS SageMaker SDK, Docker Compose, Kubernetes, OpenShift 4, Tekton, Flux, Helm charts, JSON/XML technology, Nginx, Apache Spark, OpenAI (GPT Plus, DALL-E 3, Whisper), GAN, GitHub Copilot, AI/machine and deep learning, Jupyter notebooks, TensorFlow 2, Colab, Keras API, Prometheus, Grafana, Conda, Python 3.9, PySci stack (NumPy, pandas, Scikit-learn, matplotlib)
• Responsible for webinar:
• IoT edge computing: architecture, components, resources, management
• IoT edge computing with MicroK8s, designing and creating flows/diagrams for AWS, three-step model for IoT ecosystem
• IoT processes, connectivity, data transfer and deployment, security
• Optimization of edge computing for IoT networks and services (AWS SQS queue, SNS notifications, events, analytics, buttons, device management/defender, Things Graph)
• Machine/deep learning frameworks (models, training, pipeline optimization, deployment in the cloud/at the edge (OpenShift), monitoring workloads with Prometheus and Grafana)
• Performance optimization for low latency/resilience using adaptive ML/DL/RL models for customer IoT data
• Analysis of large sensor data sets with Apache Spark, Kafka clusters
• Kasten K10 data management platform on Kubernetes multi-cluster with Helm chart, deployment, backup/disaster recovery (RTO/RPO), data lifecycle and security management
• Implementation of multilayer artificial neural network (ANN) with TensorFlow 2 and Colab for regression and classification; data analysis and provisioning for applications; development of models for testing and training, deployment of models
• Automation of business streamline processes with AI (Azure OpenAI, Discord bots/Zapier apps AI assistants (IntelliJ, GitHub Copilot))