Energy trading companies operate on thin margins and fast decisions. MET Group, headquartered in Switzerland with operations across 15 countries, has built its data function around that reality. Instead of a centralised team that hands down standards, they embedded data professionals inside business units and connected them through a network of champions who meet regularly to share what works.<\/p>\n
The approach required hiring differently. Thomas Bode, the company’s Chief Data and Technology Officer, has described the role as requiring commercial fluency alongside technical depth. The people MET hires sit in the business, report into the business, and are expected to solve business problems with data tools. A data engineer in this model needs to explain why a pipeline matters to a trading desk. A business analyst needs to read a schema.<\/p>\n
Several mandates we took in commodities and manufacturing over the past year carried the same requirement. Companies want data professionals who can walk into a commercial meeting and hold their own. The job descriptions mention Python and SQL, but the interviews test whether candidates can translate between a warehouse schema and a margin calculation. One client told us their recent data hires all came from operational roles where they had picked up technical skills, because those candidates understood what the numbers meant to the business.<\/p>\n
A portfolio of Kaggle competitions or a master’s in machine learning opens doors. The roles paying the highest premiums go to people who have shipped something that moved a commercial metric. Experience in a trading environment, a supply chain, or a finance function carries weight when a hiring manager is building a federated model like MET’s.<\/p>\n
Companies building data teams in DACH should decide early whether they need a central function that governs or embedded professionals who deliver. MET chose the latter, and it shaped every hire they made.<\/p>\n