Designed and implemented large-scale data pipelines for:- 2022 Porsche AG: Designed, implemented and benchmarked table partitioning strategy using Cloudera Impala & Apache Iceberg. This resulted in dramatic reduction of query execution time.- 2021/2022 EDEKA GmbH: Built internal data quality tools to flag erroneous, incomplete and otherwise problematic transactions for data governance and compliance using Azure Functions, Data Factory and Databricks Spark.- 2021 Covestro AG: Implemented an incremental ELT data pipeline for chemical data. Built unit and end-to-end tests enabling fast and safe changes to complex data transformation rules.- 2020 Deutsche Telekom: Designed and implemented a data pipeline for a customer review sentiment and core topic analysis using Apache Spark and Azure Sentiment Analysis API.- 2019 Allianz Deutschland: Migrated a supply chain database from a relational (PostgreSQL) to a graph based database management system (Neo4j). Refactored key queries to improve performance, reducing execution time from several days to just seconds.

- Designed and implemented a high throughput streaming solution for IoT time series sensor data- Automated scheduling of analytic workload implementing massive aggregation calculations- Worked closely with the ML Engineers to productionize Deep Learning models

- Migrated an on-premises Hadoop, Spark, MQTT, and HBase cluster to equivalent services in GCP- Developed CI/CD pipelines all the way to prod supercharging the development

Designed and implemented a collaborative video annotation tool for researchers in the fields of video intelligence and real time sensoric activity recognition.

Enhanced user experience by implementing real-time audio track and subtitle switching with multi-language support for a video portal.

Tackled various UI and performance bugs in an Android application.

Technical design and development of Android apps.