Data Platform Engineer – IAAI

Application Open:

Full-Time

MBZUAI is seeking a motivated and technically grounded Data Platform Engineer to support the design, development, and operationalization of an agricultural data platform underpinning AI-enabled research and deployment initiatives. The role will work closely with an Applied Research Scientist responsible for platform design and governance, translating conceptual architectures into reliable, scalable, and maintainable data pipelines and systems. This position is well suited for an early-career engineer with strong foundational skills, intellectual curiosity, and an interest in building data infrastructure for real-world, high-variability environments, including low- and middle-income country contexts.

Key Responsibilities

Data Pipeline Development and Implementation

• Implement end-to-end data pipelines for data ingestion, processing, storage, and retrieval in alignment with platform architecture and design specifications.
• Develop and maintain ETL/ELT workflows to support structured and semi-structured agricultural data from multiple sources.
• Ensure pipelines are robust to incomplete, noisy, or heterogeneous data commonly encountered in real-world data collection settings.

Platform and Infrastructure Support

• Contribute to the development of core data platform components, including databases, data lakes, APIs, and integration layers.
• Work with cloud-based or on-premise infrastructure to support scalable and secure data storage and access.
• Assist in deploying, monitoring, and maintaining platform services in collaboration with senior engineers or infrastructure teams.

Data Quality, Sanitization, and Validation

• Implement data validation, sanitization, and quality checks as defined by platform standards and governance requirements.
• Support schema enforcement, metadata management, and basic provenance tracking for onboarded datasets.
• Identify data anomalies or pipeline failures and contribute to debugging and remediation efforts.

Collaboration and Translation

• Work closely with the Applied Research Scientist to translate conceptual designs, governance requirements, and data standards into executable engineering solutions.
• Collaborate with AI researchers and program teams to ensure data pipelines meet downstream modeling and analytical needs.
• Document pipeline logic, system configurations, and operational procedures to support maintainability and knowledge transfer.

Learning and Continuous Improvement

• Actively develop skills in data engineering best practices, platform design patterns, and responsible data handling.
• Incorporate feedback from code reviews, system evaluations, and cross-functional discussions to improve implementations over time.
• Stay informed about emerging tools and approaches relevant to data platforms and applied AI systems.

Academic Qualifications Required

• Master’s degree in computer science, data engineering, information systems, software engineering, or related field.
• A doctoral degree is preferred but not mandatory.

Professional Experience Required

Essential:

• Minimum of four (4) years of progressive experience managing high-performance computing (HPC) environments, large-scale computing infrastructure, or advanced research computing systems.
• Strong foundational knowledge of programming concepts and data structures.
• Familiarity with at least one programming language commonly used in data engineering (e.g., Python, Java, or similar).
• Basic understanding of databases, data pipelines, or distributed systems

Preferred:

• Exposure to cloud platforms, data processing frameworks, or workflow orchestration tools through coursework or projects.
• Experience working with real-world datasets that are incomplete, noisy, or heterogeneous.
• Interest in applied data systems, AI-enabled applications, or development oriented contexts.