Minimum Academic Qualifications:

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a closely related technical field
  

Experience:

• Applicants should possess at least 5 years of professional experience in data engineering, with demonstrated responsibility for designing and operating complex data pipelines and data platforms.
  
• Strong experience designing and implementing data ingestion, transformation, and processing pipelines (ETL/ELT) for large and heterogeneous datasets.
  
• Proficiency in Python and SQL, and experience with data processing frameworks and tools commonly used in modern data engineering environments
  

Data Pipeline Design and Implementation

• Design, implement, and maintain robust data ingestion and processing pipelines for heterogeneous data sources, including soil, weather, agronomic, geospatial, and related contextual datasets.
  
• Develop scalable ETL/ELT workflows to transform raw data into structured, validated, and analytics-ready formats.
  
• Ensure pipelines support both batch and, where required, near-real-time data processing.
  
• Implement data versioning and lineage tracking to support reproducibility and auditability.
  

Cloud-Based Data Infrastructure

• Design and manage cloud-native data architectures, including data lakes, data warehouses, and analytical storage solutions.
  
• Optimize data storage and processing for performance, cost efficiency, and scalability.
  
• Support deployment of data pipelines across development, testing, and pilot environments.
  
• Collaborate with platform teams to ensure infrastructure aligns with DPI principles and interoperability standards.
  

Data Quality, Governance, and Reliability

• Implement automated data quality checks, validation rules, and monitoring to ensure accuracy, completeness, and consistency.
  
• Support enforcement of data governance requirements, including access controls, permissions, and audit logging.
  
• Work with policy and governance partners to ensure technical implementations align with data protection and consent frameworks.
  
• Proactively identify and remediate data reliability risks or bottlenecks.
  

Enablement of AI and LLM-Based Systems

• Prepare and serve data in formats optimized for AI and LLM-based advisory systems, including retrieval-augmented generation (RAG) pipelines and structured knowledge services.
  
• Support model evaluation, benchmarking, and experimentation workflows.
  

MLOps Support and Operational Readiness

• Contribute to MLOps workflows by supporting data versioning, pipeline automation, and integration with model deployment and evaluation processes.
  
• Implement monitoring and logging for data pipelines to support observability and issue diagnosis.
  
• Support reproducible experimentation through consistent data environments and pipeline automation.
  

Documentation, Collaboration, and Delivery

• Produce clear technical documentation covering data architectures, pipeline logic, and operational procedures.