- Refactor and redesign Edge computing platform for fleet charging of Eletric vehicles with metering, monitoring and Smart Charging

- Implement real time Telematics ingestion with Kafka

- Redesign data processing workflows migrating batch processing ETL to streaming

- Design data platform for stream processing with dynamic querying model for metering, telematics and chargepoint data 

• Advise projects in using Kafka as a technology and help design streaming applications
• Operationalise migration to Kafka on Openshift and Confluent Cloud Implement MVPs with Kafka
• Implement IaC pipelines for deployment of topics, connectors and ACL in Confluent Cloud

• Analyse requirements for a high frequency logging and metering platform
• Conceptualise and design a data processing platform based on Kafka, Kubernetes, Elastic Search, running on Azure services 

• Analyse requirements for a high frequency trading platform
• Conceptualise and design a data processing platform based on Kafka, Spark, Elastic Search, running on AWS services
• Implement a high frequency timeseries MVP with QuestDB for market data analysis

• Analyse requirements for a high frequency payment platform
• Implement a data processing platform based on Kafka, Spark, Elastic Search; Postgres and Redshift Spectrum, running on AWS services
• Evaluate distributed tracing with Jaeger in existing ELK stack

• Analyse requirements for a financial transaction processing platform
• Conceptualise and design a secure processing platform based on Kafka and Kubernetes on AWS
• Implement ingestion with Kafka and Spark
• Implement ELK set up in AWS with Logstash and CloudWatch integration
• assessment of implementation efforts and cost

• Conceptualise Cloud Migrations strategies for group IT with 300 FTEs and 800+ applications
• Develop and conceptualise workflow and process following the DevOps and FastFlow strategies to increase implementation and time to market speed
• Evaluate Cloud platforms (GCP, AWS, Azure) for CICD and deployment targets for applications

• Implement an event driven, stream processor for high frequency events on Kafka/ Spark for S3
• Implement Redshift Spectrum/ Glue Crawler based Data Lake for events with continuous schema evolution
• Assess requirements and design cloud based data processing docker components with AWS ECS
• Implement distributed logging with the ELK stack, integrating Prometheus, CloudWatch and log metrics in Kibana
• Design and implement Service Deployment Automation with Terraform
• Implement Python based Applications with AWS Lambda
• Implement data pipelines with AWS Kinesis and AWS SQS
• Migrate existing data infrastructures from relational databases and Classic Redshift to the new Data Lake

• Analyse requirements for a high frequency trading platform
• Conceptualise and design a data processing platform based on Kafka, Spark, Elastic Search; Postgres and Redshift Spectrum, running on AWS services, assessment of implementation efforts and cost

• Implement a micro service centric event sourced platform for a paper publication system
• Assess requirements and design cloud-based data processing docker components with AWS ECR
• Design and implement Service Deployment Automation with Terraform
• Develop a concept for Monolith migration to microservices using strangulation pattern
• Implement components with TDD on Java 8 with Kinesis, Elastic Search, PostgreSQL, AWS Lambda
• Implement log distribution with filebeat, logstash with grok-patterns
• Implement log and metrics dash board in Kibana with Elastic Search
• Implement Python based Applications with AWS Lambda
• Implement data pipelines with AWS Kinesis and AWS SQS
• Refactor and migrate microservices to Docker centric deployments

• Asses requirements and design a cloud-based data processing platform
• Design and implement an integrated data platform for adidas digital services based on AWS components such as EMR (Hadoop, Spark), Elastic Search
• Introduce and implement Apache Kafka as a message platform
• Design and Implement Terraform and ansible based immutable architecture

• Asses requirements and design a cloud-based data processing platform
• Design and implement Service Deployment Automation with AWS CloudFormation and Ansible
• Introduce and implement Apache Kafka as a message platform
• Implement a high volume, near real time data processing pipeline from RDBMS bin log, JMS and Kafka data sources into Redshift and AWS S3 using Apache Spark Streaming on Scala
• Implement real ? time Geo - Tracking applications for vehicles with Elastic Search and Spark

• Asses and optimise processing infrastructure
• Implement a real-time anomaly detection mechanism for current and voltage meters with Spark Streaming and Apache Kafka on Scala
• Implement a Cassandra/Spark based aggregation platform for voltage and current meters for a domestic utility provider
• Implement a high volume (1 M records/sec) time series archive on top of Cassandra
• Optimise Cassandra access and keyspace design
• Implement a Play Framework based REST API for high volume queries

• Implement probabilistic identification mechanisms for App and Web users in profiling/ tracking records with Spark on Scala
• Implement a large-scale Cassandra/Spark based aggregation platform for web and app user reporting and analytics
• Implement a Play Framework based REST API for high volume queries
• Optimize Cassandra access and keyspace design
• Implement high volume SSTable based bulk Loading for large Cassandra Clusters
• Implement automated AWS Elastic Map Reduce based pipelines with EMR and Spark
• Implement Session and Cookie restoration mechanisms for user tracking records

• Analyse requirements for a data processing platform
• Conceptualise and design a data processing platform based on Hadoop, Spark, Elastic Search; assessment of implementation efforts and cost
• Develop a concept for Multicloud deployments in GCP and AWS