Description
Key Skills: Python, SQL, ArcGIS, QGIS, FME, PostGIS, GIS Data Engineering, Spatial Analysis, Cloud Services, AI/ML
Good to Have Skills: GitLab, CI/CD, code reviews, documentation, production support, distributed data processing patterns, validation frameworks, monitoring, observability, operational reliability, automation strategies for large-scale geospatial workflows, system design and architecture skills for scalable geospatial data pipelines, stakeholder alignment skills.
Roles & Responsibilities:
- Define and evolve the architecture, design patterns, and technical direction of complex spatial data processing pipelines and frameworks used in map creation and maintenance.
- Drive modernization of spatial data workflows to improve scalability, maintainability, interoperability, and operational efficiency across multiple processing areas and source types.
- Lead complex geospatial analysis to detect change, identify systemic quality issues, resolve high-impact data and pipeline problems, and improve map content reliability at scale.
- Define and advance rule-based, data-driven, and AI/ML-enabled approaches for feature matching, attribute comparison, change detection, prioritization, confidence scoring, anomaly detection, and assisted decision-making.
- Define validation frameworks, observability practices, performance measures, and reliability standards to improve production confidence, traceability, and data quality across processing pipelines.
- Guide solution design, promote reusable patterns and platform thinking, influence engineering standards and best practices, and support technical alignment across geospatial data engineering.
- Partner with engineering, analytics, product, operations, and adjacent technical teams to align roadmaps, deliver strategic capabilities, and enable adoption of scalable workflow improvements.
- Evaluate emerging technologies, data engineering approaches, and AI/ML opportunities, and translate them into practical technical direction for the domain