Data Engineer (Geospatial)

Responsibilities

• Requirements Analysis and Solution Design
• Collaborate with business users, data analysts, and relevant stakeholders to gather and document requirements for analytics use cases.
• Translate business needs into clear technical specifications and design suitable data architecture solutions that align with the organisation's overall data strategy and governance frameworks.

• Data Modelling and Architecture
• Design and implement appropriate data models based on analytical and business requirements.
• Develop suitable table structures and relationships according to business needs and expected query patterns.
• Consider performance, scalability, and maintainability when making data modelling decisions.
• Work closely with stakeholders to understand their data requirements and translate them into effective database designs.

• Data Pipeline Architecture and Design
• Design end-to-end data pipelines that transfer data from various source systems into the different layers of the data analytics platform, including raw, technical, and business layers.
• Ensure that pipeline designs follow established best practices for scalability, reliability, and maintainability while complying with data governance and security requirements.
• Data Pipeline Development and Implementation
• Develop, test, and deploy data pipelines using modern data engineering tools and frameworks.
• Implement data transformation logic, data quality checks, and error-handling mechanisms to support reliable data processing.
• Build automated workflows that can manage both batch and real-time data processing requirements.

• Data Infrastructure Maintenance and Optimisation
• Monitor and maintain existing data pipelines to ensure strong performance, reliability, and cost-effectiveness.
• Investigate data quality issues, resolve pipeline failures, and implement improvements to optimise system performance.
• Conduct regular reviews of the data infrastructure to identify opportunities for enhancement and modernisation.

Qualifications

• Minimum of 3–5 years of experience in data engineering, data analytics, or a related technical field.
• Demonstrated experience in designing and implementing data pipelines and analytics solutions.

Required Skills

• Data Platform Technologies
• Comprehensive understanding of modern data architecture patterns, including Data Lakes, Data Warehouses, Data Lakehouses, and Data Mesh.
• Ability to assess and recommend suitable architectural approaches based on business requirements and technical constraints.

• Technical and Programming Skills
• Proficiency in Python, with experience using data manipulation libraries such as pandas and NumPy, data pipeline frameworks, and API development.
• Strong SQL skills for data querying, transformation, and database management across different database platforms.

• Data Engineering Practices
• Strong foundation in data engineering principles, including data ingestion, transformation, quality assurance, and pipeline orchestration.
• Knowledge of data analysis techniques and data management practices, including data governance, lineage tracking, and metadata management.

• Cloud and Platform Experience
• Knowledge of Databricks and AWS cloud services would be an advantage.
• Experience with cloud-based data processing, storage solutions, and managed analytics services would be beneficial.
• Familiarity with infrastructure-as-code and DevOps practices for data platforms.

• Problem-Solving and Communication
• Strong analytical and problem-solving abilities, with the capability to troubleshoot complex data issues and optimise system performance.
• Excellent communication skills and the ability to work effectively with both technical and non-technical stakeholders.
• Ability to translate business requirements into appropriate technical solutions.

Preferred Skills

• Experience working with GIS platforms such as ArcGIS, geospatial APIs, routing engines, or 2D and 3D mapping technologies would be a bonus.