Meter Data Technician
Pubblicato il 01-09-2026 - Serco in Italia
Serco is a trusted leader in Space Services, with a proud history spanning over 55 years and a track record of supporting more than 50 space missions for world-renowned agencies like ESA and NASA. Our expertise covers the entire space lifecycle - from satellite testing and launch services to spacecraft operations, space surveillance, and data management - making us a key partner in advancing national strategies and driving innovation in the New Space era. The position holder will work within the contractual framework of the Swarm Data Innovation and Science Cluster (DISC), supporting our client, the European Space Agency (ESA). The role is based at ESA's Earth Observation centre, ESRIN, in Frascati, Italy, with partial teleworking options available. The ESA Swarm mission, launched in November 2013, uses a constellation of three identical satellites to provide survey of Earth's magnetic field, its evolution, and the atmosphere's electrical environment. The mission aims to understand the Earth magnetic field's origin, monitor its changes,
to analyse ionospheric plasma parameters and electric fields, and to monitor space weather events.
The post-holder will be supporting the data quality assessment of the Swarm different instruments, taken from a subset of: magnetic field instruments, composed by an Absolute Scalar Magnetometer (ASM) and a Vector Field Magnetometer (VFM) and Electric Field Instrument (EFI), composed by a Thermal Ion Imager (TII) and two Langmuir Probes (LP) and of Swarm higher level geophysical products, e.g. Level 2 and advanced products. The main activities will be:
Operations of the Quality Control (QC) tools for systematic and on-request product quality analysis and reporting.
Support to data reprocessing campaigns: dataset definition, analysis of reprocessed data.
Support the Swarm Instruments Performance and Product Quality Expert, the ESA Post Launch Support Office (PLSO) and the ESA Flight Operation Segment (FOS) on sensor and data anomaly inve