References

As IT Consultant I am wearing many hats when embarking on a project. Due to the confidential nature of my assignments it is often not possible to talk about what I did, when, for which client. To give you an idea what I can provide let me introduce you to a set of anonymized case studies of real projects I successfully completed.

Organizational and Product

Challenge: Slow and inflexible IT department

Solution
  • Due diligence and analysis together with client before any action.
  • Reorganization of department into product teams.
  • Emphasis on agile practices like Scrum, continuous delivery and lean product development, Pre- and Post-Mortems, Tech Radar.
  • Case-study-based hiring process.
  • Creation of common guidelines for the teams (Rest, QA).
Outcome Much faster feature development. Less bugs and improved customer satisfaction. Happy teams.

Challenge: Constantly failing product release dates for B2C application

Solution
  • Change of culture to allow teams to raise concern openly so that timelines can be adjusted. Honest prioritizing of features.
  • Lean product development.
  • Going live with a subset of features and developing features together with customer.
Outcome Product roadmap that can be used by Sales and Market for planning purposes. Successful product release.

Challenge: Creation of B2B software product that failed before

Solution
  • Design sprint with customers to test and verify ideas.
  • Using lean practices to prioritize features.
  • Moving team into full-stack direction using React and NodeJs.
  • Questioning requirements and keeping things as simple as possible.
Outcome Application went live with a stable team that maintains and implements features. The automated processes free-up resources and save time for both the company and the customers.

Tech

Challenge: High performance big data analysis pipeline in crisis mode

Solution
  • Analysis of real problems and drafting a technical roadmap to go to market as soon as possible with the most important features first.
  • Replacement of batched approach (Spark, Hadoop) with in-memory real time calculation and aggregation. Outcome was faster development pace and quicker bug fixes.
  • Hands-on developed with a team of the client for long term maintenance.
  • Usage of standard components (PostgreSQL, Kafka, Spring Boot) so that the system can be run on AWS, but also on bare metal hardware for cost savings or performance increases.
Stack Kafka, Spark, Spring Boot, Java, Scala, PostgreSQL, Gatling, AWS, Datadog, DynamoDb

Challenge: Graph-based tag-management system fails because of more load

Solution
  • Analysis of database system and isolation of performance problem.
  • Prototype with PostgreSQL and LTree data structure to store and retrieve data and fix performance problems.
  • Performance tests using gatling to prove performance of new system.
  • Migration of data and successful rollout to customers.
Stack MySQL, PostgreSQL, Java, Php, AWS, RDS, Ltree, Gatling, Spring Boot, Graph data structures, Rest Api

Challenge: Software prototype of company ended up in production but has severe stability issues

Solution
  • Prototype was too successful and the clients of the customer love it, but the services and software written was not intended to be scaled.
  • Deep analysis of business requirements and current implementation.
  • Prioritization of problems with highest impact.
  • Redesign of services and re-implementation of unstable systems using standard components.
  • Migration of data from NoSQL to SQL.
  • Migration from bare metal to AWS.
Stack ElasticSearch, Php, Java, Javascript, Spring Boot, Tikka, Lucene, RDS, AWS, PostgreSQL, React