MapReduce Experts 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.

OpsWorker turns Kubernetes alerts into root-cause analyses, on top of the monitoring a team already runs. I lead the technical side: the agent architecture, the AWS infrastructure it runs on (fully inside EU regions), and the engineering decisions behind it, read-only in the cluster by default, human in the loop for judgment. The stack underneath: Amazon Bedrock and Bedrock AgentCore, agents built with the Strands Agents SDK, the Claude and OpenAI APIs, and the Kubernetes API.

• Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
• Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
• Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
• Use of Scrum and Kanban methods.
• Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
• Development of new plugins and add-ons needed on current infrastructure.
• Database support.
• Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
• Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
• Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
• Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
• Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
• Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
• Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
• Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
• Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
• Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
• Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
• Automated system provisioning and deployment using CloudFormation templates.
• Configuration of IAM roles, policies and permissions to ensure secure access control.
• Patch management, backup automation and disaster recovery setup on AWS infrastructure.
• Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
• Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
• Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
• Configuration of AWS CloudWatch to monitor application performance and system events.
• Planning and execution of migration of on-premises applications to AWS cloud platforms.
• Deployment of containerized applications using Docker and Kubernetes in AWS environments.
• Deployment of internal software packages between availability zones using AWS CodeDeploy.
• Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.

• 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

• Architected end-to-end credit risk pipelines by orchestrating Airflow ETLs and training LSTMs/Transformers to predict default and prepayment speeds on MBS portfolios.
• Developed Deep Learning NLP solutions using BERT and LayoutLM for document processing, leveraging Transfer Learning and custom PyTorch loss functions to automate underwriting.
• Optimized R&D lifecycles through Bayesian tuning, Batch Normalization, and MLflow tracking to ensure robust model performance throughout volatile mortgage market cycles.
• Productionized scalable MLOps infrastructure via Docker and INT8 Quantization, deploying low-latency FastAPI microservices on AWS SageMaker with automated CI/CD pipelines.
• Ensured regulatory compliance by integrating SHAP/LIME for explainability and establishing real-time Data Drift monitoring to meet strict FHFA and Fair Lending standards.

• Help developers with individual live coaching to become pro-level Apache Spark engineers
• Organize and host multi-day, tailored Apache Spark workshops for development teams
• Create educational technical content on a self-hosted blog, YouTube, and social media

• Building teams of data people - BI Analysts, Data Scientists, Data Engineers
• Defining data strategy across all business units to support short, mid & long-term business goals
• Collaborating with the product leads & management & heads of departments to provide data support
• Defining budget to make everything happen
• Aligning the data teams goals with company vision, strategy & objectives
• Responsible for the data governance as well as for the strategic development planning
• Defining and developing joint OKRs
• Reporting directly to the CTO & CEO

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

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

• Development of a Cloud Management Platform (CMP) that can create a private/mixed cloud of any complexity based on a textual description with one click or interactively.

• CMP also includes the complete hardware management cycle.

• As a foundation, it uses Kubernetes, OpenStack, and Hadoop.

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

• The private cloud can run any customer workloads, including a full Hadoop stack with HDFS, Spark, MapReduce, Mesos, HBase and around 20 other ML/DL technologies.

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

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

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

• 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).

• Global marketing analytics for Hitachi Energy as part of a global data modernization initiative aiming to enhance data retention, historical data availability and provide Eloqua's 2-year retention for remote interaction reporting and analytics.

• Analyzed Eloqua's default retention policy and identified risk of data loss for records older than two years.

• Designed and implemented historical data preservation strategy by creating transformed tables in the target data platform to archive older data while ensuring data quality dashboards.

• Collaborated with the Power BI team to re-point dashboards from raw Eloqua imports to the newly created archival layer.

• Leveraged Jira to track and manage data engineering tasks, bugs, and feature requests across Agile sprints; coordinated backlog prioritization and task assignment to align data pipeline development with business needs.

• Power BI dashboard optimization:

• Worked closely with business stakeholders to assess and understand reporting needs for reverse customer data.

• Designed and implemented incremental refresh in Power BI to ensure daily updates without full data reloads.

• Collaborated with Azure data engineers to optimize data processing and publication pipelines.

• Stakeholder communication & data modeling:

• Acted as liaison between Group Data Office and Technology Office to align data modelling standards.

• Gathered requirements from data engineering team and participated in weekly status meetings to provide implementation updates and resolve blockers across teams in Germany, Poland, and India.

• Documentation & quality assurance:

• Prepared end-to-end technical design documentation, data flow diagrams, and Power BI audit guides for future reference.

• Participated in UAT sessions with business users to validate data outputs and report accuracy.