Senior Data Engineer / MLOps

6/2022 – 12/2022

Cluster Migration of Internal Data Warehouse

As data volumes continue to grow for eCommerce companies and the number of data consumers within the
organization is increasing, sometimes old infrastructure will not be able to keep up with the challenges.
Additionally, In this particular case, the computing and warehousing cluster has to be on-premise for data
security reasons. After new cluster-infrastructure had been provided by an external provider, all data warehouse
and computing logic has to be migrated from the old infrastructure to the new infrastructure. An additional
challenge is to maintain backwards compatibility of the migrated processes at all times.

Key Achievements:
• Migration and Deployment of 30+ airflow DAGs with 20 – 50 Tasks each on new infrastructure
• Co-development of a python client library for Apache Livy that is used by 100+ airflow tasks
• Deployment of 20+ Apache Hive databases with 10 – 50 tables each in three Data Warehouse layers via
Ansible
• Code review of 5-10 merge requests per week

Technologies: Apache airflow, Python, Apache Hive, Apache Spark, PySpark, Apache Livy, Apache Hadoop,
Ansible

Ansible, Apache Hadoop, Apache Spark, Python

2/2020 – 6/2022

Platform for Real Time Fraud Detection in eCommerce

In order to prevent financial and reputational loss in eCommerce platforms an automated detection of fraud
patterns in online shops is needed. The software should be able to scale out over multiple shop systems and data
sources. Further requirements are monitoring traffic in real time and incorporating expert knowledge alongside
machine learning models.

Key Achievements:
• Lead design of the platform
• Implementation of a proof of concept from which 80% of code made the first product iteration
• Technical Lead for a team of 5 Developers
• Successful deployment and zero downtime operations on customer premises at around 15 million events
per day
• Design of cloud based testing environment that can be brought up in less than 15 minutes (Infrastructure
as Code) and handle up to 10 times of production workload

Technologies: Apache Flink, Apache Kafka, Redis, Terraform, AWS, kubernetes, helm docker, Datadog

Amazon Web Services (AWS), Docker, Flink, Kafka, Kubernetes

6/2018 – 2/2021

Webtracking Event Pipeline with snowplow in AWS

For an eCommerce Platform it is crucial to have a detailed picture of customer behaviour on which business
decisions can be based. Either in real-time or from the data warehouse. For that a flexible, scalable, and fieldtestet solution is necessary which can run in the cloud. Additionally, all browser events need a custom
enrichment with business information from the backend in order to provide necessary context e.g. for „Add to
Cart“-events.

Key Achievements:
• Integration of snowplow event-pipeline in cloud based shop architecture
• Day to day operations of event-pipeline at ca. 4 million events per day
• Co-Engineering of custom enrichment in the webshop backend (ca. 1000+ lines of code) and handover
of ownership to the backend team
• Setup of custom real time event monitoring (< 1s latency) with elasticsearch and kibana
• Setup of custom scheduling and deployment processes for 5 components of the snowplow event-pipeline

Technologies: snowplow, kubernetes, amazon EMR, amazon kinesis, amazon redshift, Apache airflow, kibana,
elasticsearch, NodeJS, Gitlab CI

elasticSearch, Kubernetes