Senior Data Engineer

5/2021 – 10/2021

• Guide customers through the process of migrating to Snowflake and develop methodologies to improve the migration process
• Deploy Snowflake following best practices, including ensuring knowledge transfer so that customers are properly enabled and are able to extend the capabilities of Snowflake on their own
• Work hands-on with customers to demonstrate and communicate implementation best practices on Snowflake technology
• Maintain deep understanding of competitive and complementary technologies and vendors and how to position Snowflake in relation to them
• Work with System Integrator consultants at a deep technical level to successfully position and deploy Snowflake in customer environments
• Provide guidance on how to resolve customer-specific technical challenges
• Support other members of the Professional Services team develop their expertise
• Collaborate with Product Management, Engineering, and Marketing to continuously improve Snowflake’s products and marketing.

Snowflake, Amazon Web Services (AWS), Cloud Computing, Data Warehousing, ETL, Google Cloud, Microsoft Azure, Solution Architektur

1/2019 – 4/2021

Design and building a data lake on Microsoft Azure Cloud:

• Build ETL pipelines with Azure Databricks, Azure Data Factory, Azure Functions and Talend Data Management
• Data integration from variety of data sources (e.g. relational data from databases such as IBM DB2, MS SQL Server, SAP BW and semi-structured data from web services such as weather data, check24 data)
• Deployment of Azure Database for PostgreSQL for relational data within the data lake
• Deployment of Azure Data Lake Storage gen. 2 for unstructured / semi-structured data within the data lake

Apache Spark, Big Data, Cloud Computing, Data Warehousing, ETL, Java (allg.), Microsoft Azure, Python, Snowflake

12/2017 – 12/2018

Building a scalable big data pipeline moving streaming data and batched data to a data lake on AWS Cloud:

• Data integration from variety of data sources IBM Mainframe, Oracle, MS SQL Server and SAP BW with Informatica Data Integration Tool and AWS Glue
• Develop python application for automatic generation of Informatica mappings, sessions and workflows
• Deployment of Streaming Platform Confluent Kafka, which is one of the main building blocks for data ingestion process
• Deployment of a Snowflake Data Cloud in AWS Cloud Platform for structured and semi-structured data within the data lake
• Deployment of AWS S3 storage for unstructured and semi-structured data within the data lake

Amazon Web Services (AWS), Apache Spark, Big Data, Cloud Computing, Data Warehousing, ETL, Python, Snowflake

3/2016 – 11/2017

Building a big data platform on Azure HDInsight platform:
• Extract, transform and load data from variety of sources (Oracle, MS SQL Server, SAP BW) to Azure blob storage
• Use HDInsight tools Spark und Hive for transforming and storing data from the Azure blob storage to Hadoop-based storage (HDFS)
• Support data scientist team building a Hadoop-based analytical platform leveraging the analytic tools such as Spark Machine Learning, Apache Mahout

Support Proof of Concept activity designing a machine learning based predictive model for credit card fraud detection leveraging Azure HDInsight platform:
• Design a predictive model using HDInsight tools such as HDFS, HBase, Kafka, Spark
• Build a data pipeline, which extract the master data from HBase noSQL database through Kafka streaming platform to Spark for batch processing
• Build a data pipeline for the transaction data, which is streamed through Kafka streaming platform to Spark for real-time Processing
• Use Spark Machine Learning library to model a prediction for credit card fraud detection

Apache Hadoop, Apache Spark, Big Data, Cloud Computing, Data Warehousing, ETL, Python, SQL

6/2008 – 2/2016

• Provide technical support to the customers (Vodafone Group, Deutsche Telekom, Telefonica O2) for deploying and testing of ZTE products

• Organize Proof of Concept with the customers to demonstrate ZTE product capabilities and to specify their potential use cases

• Team management in the ZTE data-based product development

• Planning and organization of pre-sales activities and meetings with suppliers (Qualcomm, Intel, Microsoft and Google)

Technischer Vertrieb