Daniel is leading the Big Data and Cloud Machine Translation group at SDL and in the last two years he was involved in building a highly scalable Machine Learning platform using some technologies from the BigData ecosystem like Kafka, HBase, Hadoop HDFS, ELK, Mesos in combination with new architectural patterns such as micro-services. Previously he worked on a Hadoop-based data pipeline that processes Petabytes of unstructured data; the resulting clean data is used to increase the quality of SDL’s Statistical Machine Translation engines. Daniel is also involved in the local Big Data Cluj Romania community as a meet-up organizer/speaker in the effort to raise the awareness of the Big Data, Hadoop, Spark, Machine Learning fields.