ICT Associate Geospatial Data Engineer

Job Highlight

Under the overall supervision of the UNOPS Senior Programme Manager, this position is in the United Nations Information and Communications Technology Facility (UNICTF) in Valencia, Spain. The incumbent will work under the guidance of Chief, Data Management Unit and direct supervision of ICT Senior Officer – GIS (UNOPS) or designated officer. The incumbent of this position will be a staff member of UNOPS under its full responsibility. THIS IS A LOCAL POSITION FOR WHICH ONLY APPLICANTS WHO ARE SPANISH OR EUROPEAN UNION CITIZENS OR APPLICANTS WHO POSSESS A VALID RESIDENCE AND WORK PERMIT IN SPAIN ARE ELIGIBLE FOR CONSIDERATION.

Role Purpose

The ICT Associate - Geospatial Data Engineer contributes to the delivery of reliable, timely, and quality-controlled geospatial and IoT data to support operational systems and decision-making across UN field missions. The role ensures that data pipelines, datasets, and services are accurate, consistent, and fit for purpose, enabling downstream applications, analytics, and enterprise platforms.

Functions / Key Results Expected

With the delegated authority, the ICT Associate – Geospatial Data Engineer’s key functions are:

Summary of Key Functions:

• Analysis and Data Engineering
• Innovation and Data Integration
• Data Quality, Maintenance and Reporting

Analysis and Data Engineering

• Assists in designing, developing and maintaining end-to-end geo-enabled IoT data pipelines, including ingestion, transformation, integration, and delivery across multiple data sources.
• Supports in developing and managing data models, datasets, and repositories (data lakes/lakehouses), ensuring scalability, performance, and alignment with enterprise architectures.
• Helps in processing and integrating large-scale structured, semi-structured, and time-series IoT data, including data from industrial platforms (e.g., PI System or equivalent).
• Support in applying spatial and temporal analysis techniques to detect patterns, anomalies, and operational insights from geo-enabled IoT datasets.
• Helps in ensuring that datasets are properly structured, geo-referenced, standardized, and documented in line with established data models and governance frameworks.
• Assists in delivering reliable, analytics-ready datasets and data services to support operational systems and decision-making.

Innovation and Data Integration

• Helps in implementing and optimizing ETL/ELT workflows and data integration pipelines to enable efficient, automated, and scalable data processing.
• Assist teams in iterating real-time and near real-time IoT data streams into enterprise geospatial and data platforms.
• Works with teams in applying automation, scripting, and data engineering best practices to improve performance, reliability, and maintainability of data pipelines.
• Supports the evolution of modern data architectures (data lake, warehouse, lakehouse) in cloud or hybrid environments.
• Collaborates with cross-functional teams to design and enhance data solutions, ensuring interoperability between IoT, geospatial, and enterprise systems.

Data Quality, Maintenance and Reporting

• Implements and enforces data quality management frameworks, including validation rules, monitoring mechanisms, and data quality indicators (e.g., accuracy, completeness, timeliness, consistency).
• Monitors and troubleshoots data pipelines and datasets to ensure data integrity, reliability, and operational continuity.
• Maintains and updates datasets, databases, and data services in compliance with governance, metadata, and security standards.
• Supports the delivery of trusted data products, including dashboards, reports, and geospatial services based on curated and governed data.
• Prepares and maintains technical documentation, data workflows, and standard operating procedures.
• Contributes to quality assurance and peer review processes to ensure high-quality data outputs and services.
• Perform other duties as may be reasonably required and in line with the incumbent’s scope of services above.

Education Requirements

Required: Secondary school (or equivalent) with 6 years of relevant experience OR Bachelor’s degree (or equivalent) in any discipline with 2 years of relevant experience.

Desired: Industry-recognized certifications in data engineering, cloud data platforms, or data architecture (e.g., Microsoft Azure Data Engineer, AWS Data Analytics, Google Professional Data Engineer). Certifications or training in data quality management, data governance frameworks, or geospatial technologies are a strong advantage.

Experience Requirements

Required: Relevant experience is defined as experience in the field of data engineering, data pipelines, and data platform management, including modern data architectures. Experience designing and maintaining end-to-end data pipelines (ETL/ELT) and working with data lake, warehouse, or lakehouse architecture. Hands-on experience with geo-enabled IoT data, including near real-time or streaming data processing, and industrial time-series platforms such as AVEVA PI System or equivalent. Proficiency in Python, SQL, and data processing frameworks (e.g., Spark or equivalent).

Desired: Experience with geospatial data engineering, including spatial databases and GIS platforms (e.g., ArcGIS Enterprise). Experience with streaming technologies (e.g., Kafka, Event Hubs, or similar). Experience applying data governance frameworks, metadata management, and data catalogues. Familiarity with UN or large-scale operational environments, supporting mission-critical systems. Experience supporting analytics platforms, dashboards, or data services consuming engineered datasets. Experience delivering scalable, performance-optimized data solutions in cloud or hybrid environments. Strong experience in data quality management, including validation, monitoring, and data quality dimensions (e.g., accuracy, completeness, timeliness).

Key skills/competency

• Data Engineering
• Geospatial Data
• IoT Data
• Data Pipelines
• ETL/ELT
• Python
• SQL
• Data Quality Management
• Cloud Data Platforms
• Data Governance