Matija Kordic

Design and deployment of the entire cloud-native geospatial infrastructure on GCP, including establishing secure, high-performance environments (Private IP Postgres, VPCs, separate Staging/Production) Led the cost-effective migration of the backend from Azure to GCP Architected data pipelines, using Vertex AI/Kubeflow Pipelines and Cloud Run Functions for serverless processing Established robust DevOps policies, CI/CD via GitHub Actions and WIP, monorepo structure, and engineering best practices

Managed and scaled an engineering team, overseeing AWS infrastructure and delivery practices. Team included fullstack developers, DevOps, data engineers and data scientists. Defined engineering workflows, sprint practices, and coding policies. Engaged stakeholders to align technical roadmaps with product strategy

Led the development, and maintenance of the geospatial codebase Developed an algorithm for extracting polygons from jpeg and png using semi-supervised models Migrated and dockerized repositories; established DevOps practices (CircleCI, pre-commit, Commitizen). Drove technical upskilling (Python, Data Science workshops) for junior staff. Oversaw geospatial production strategies and stakeholder collaboration across teams Supervised >20 people (analysts, engineers) across international teams.

Algorithm development using Safe’s FME, SQL and python for large-scale geospatial datasets. Organised Python and SQL workshops for analysts via an interactive workshop. Led a 6-person team in developing complex solutions for stakeholders Ongoing strategising with vendors and upward stakeholders.

Initial Research and Development Phase. Data gathering, manipulation, and database design. Development of a 7-layered basemap approach.

Working on processing aerial imagery

Project management, GIS and hydrogeology analysis and report writing

Content strategy, generating help centre, training and managing the support team

Worked on developing GIS maps for various purposes (academic and professional).