Philippe is a data engineer with a chemical engineering foundation, an M.Sc. from TU Berlin and two years inside one of the most complex data environments there is: a near 300 hectares chemical park from one of Germany's biggest chemical manufacturers. There he redesigned a yield calculation ETL pipeline, tripling processing speed and improving the reliability and quality of the data behind it. That pipeline drew on manual laboratory analysis on one side and more than 600 real-time sensors on the other, which demanded as much process knowledge as engineering.
Philippe knows what yield means in a reactor, in Python and on a balance sheet. He uses that to find the data quality gaps other people walk past. He recently updated his data engineering skills, working through cloud-native architecture on AWS, orchestration in Airflow and modern modeling with dbt and Snowflake.
At InterWorks he brings that combination to client work. He builds pipelines that keep running after he steps away and documents them so the next person can pick them up. He delights in working with numbers and data, using them as means to help clients be better at what they do.
Philippe knows what yield means in a reactor, in Python and on a balance sheet. He uses that to find the data quality gaps other people walk past. He recently updated his data engineering skills, working through cloud-native architecture on AWS, orchestration in Airflow and modern modeling with dbt and Snowflake.
At InterWorks he brings that combination to client work. He builds pipelines that keep running after he steps away and documents them so the next person can pick them up. He delights in working with numbers and data, using them as means to help clients be better at what they do.