IMEO Remote Sensing Data Engineer

## IMEO Remote Sensing Data Engineer - Organization:UNEP - United Nations Environment Programme Result of Service:The Remote Sensing Data Engineer consultant will provide a reliable flow of satellite detections into MARS from both existing and newly integrated data sources; a healthy, monitored ingestion pipeline as data volume and automation grow; an owned and improved estimation of source persistency and total annual emissions for methane point sources; and automated public data products published on IMEO’s Data Platform. Expected duration:12 months Duties and Responsibilities:The United Nations Environment Programme (UNEP) is the leading global environmental authority setting the global environmental agenda and advocating for environmental action across the UN system. UNEP’s International Methane Emissions Observatory (IMEO) is a data-driven initiative accelerating reductions in methane emissions, a major contributor to climate change. IMEO provides open, reliable and actionable data to support emissions reductions aligned with the Paris Agreement. To do this, IMEO is harnessing a methane data revolution under way thanks to rapidly advancing technology and momentum for methane action. IMEO collects, integrates, and reconciles data from methane-detecting satellites, scientific measurement studies, rigorous industry reporting through the Oil and Gas Methane Partnership 2.0 (OGMP 2.0), and national emissions inventories. These initiatives are game-changers for climate action – and as a core implementing partner of the Global Methane Pledge, IMEO is shaping the future of methane mitigation. Within IMEO, the Methane Alert and Response System (MARS) connects near-real-time satellite methane detections with notifications to stakeholders. The consultant will focus on integrating new satellite data sources into MARS, including high-temporal-frequency geostationary sensors, whose detections must be ingested as grouped plume events. This includes the adjustments to the MARS PostgreSQL database schema needed to ensure smooth ingestion, processing, and storage. The consultant will also share the day-to-day monitoring and maintenance of the MARS data ingestion pipeline, which runs on Azure: as the number of automated processes grows, so does the number of ingestion issues, which the consultant will triage in Jira and resolve in production, extending the MARS data quality monitoring checks to cover the new data sources and processing steps. In addition, the consultant will own the MARS source persistency pipeline, which estimates source persistency (the fraction of satellite observations in which a source is detected emitting) and total annual emissions for methane point sources, improving it in line with the latest research directions. The consultant will also maintain and automate the public data pipelines built on these results, such as plume and source exports, the top 50 emitters list, and detection and non-detection time series. Qualifications/special skills:Required: First-level Degree (e.g. Bachelors) in Remote Sensing, Computer Science, Data Science, Physics Or a related field (optional)High proficiency in Python and its scientific and geospatial stack, applied to data processing and analysis tools, for satellite remote sensing data. This is a hands-on coding role requiring comfort with, and enthusiasm for, software developmentExperience with PostgreSQL, cloud platforms – preferably Azure, and ideally its ML and web app services – and software engineering best practices: version control (Git), maintainable and tested code, containerization (Docker), and CI/CD (Jenkins or similar)Scientific expertise in remote sensing, demonstrated through open-source projects or scientific publications, with particular interest in the retrieval and interpretation of atmospheric trace gases from satellite imageryExperience building or maintaining operational data pipelines and data quality monitoring systems, including issue tracking with tools such as Jira.A genuine interest in environmental and climate-related challenges, demonstrated through academic projects, personal research, open-source contributions or independent learning. - High proficiency in Python and its scientific and geospatial stack, applied to data processing and analysis tools, for satellite remote sensing data. This is a hands-on coding role requiring comfort with, and enthusiasm for, software development - Experience with PostgreSQL, cloud platforms – preferably Azure, and ideally its ML and web app services – and software engineering best practices: version control (Git), maintainable and tested code, containerization (Docker), and CI/CD (Jenkins or similar) - Scientific expertise in remote sensing, demonstrated through open-source projects or scientific publications, with particular interest in the retrieval and interpretation of atmospheric trace gases from satellite imagery - Experience building or maintaining operational data pipelines and data quality monitoring systems, including issue tracking with tools such as Jira. - A genuine interest in environmental and climate-related challenges, demonstrated through academic projects, personal research, open-source contributions or independent learning. Languages:English is the working language of UNEP's IMEO. Knowledge of additional languages is an asset. Additional Information:An operational ingestion workflow for high-frequency satellite data source, including the PostgreSQL database tables required to store it and the grouping of high-frequency detections into plume events.An owned and documented source persistency pipeline running in Azure ML Studio, with improved estimates of source persistency and total annual emissions for methane point sources.Data quality monitoring checks extended to the new data sources and processing steps, with ingestion issues triaged and resolved in production.Automated generation of the MARS public data products for plumes, sources and top 50 emitters, further automating the existing workflows. - An operational ingestion workflow for high-frequency satellite data source, including the PostgreSQL database tables required to store it and the grouping of high-frequency detections into plume events. - An owned and documented source persistency pipeline running in Azure ML Studio, with improved estimates of source persistency and total annual emissions for methane point sources. - Data quality monitoring checks extended to the new data sources and processing steps, with ingestion issues triaged and resolved in production. - Automated generation of the MARS public data products for plumes, sources and top 50 emitters, further automating the existing workflows.