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Data Platform Engineer – IAAI

Applications Close: Aug 17, 2026

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

Residential Building (Biofuel Block) - Masdar City - SE45 05 - Abu Dhabi - United Arab Emirates

13Views

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.

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Data Platform Engineer – IAAI

Applications Close: Aug 17, 2026

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

Residential Building (Biofuel Block) - Masdar City - SE45 05 - Abu Dhabi - United Arab Emirates

13Views

Application Open:

Full-Time

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.

This is a Preview Listing…

You must sign in to see the full job description, and to apply.

Manage / Upgrade this job to a Full Job Listing.

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Job Fulfilled

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Data Platform Engineer – IAAI

Applications Close: Aug 17, 2026

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

Residential Building (Biofuel Block) - Masdar City - SE45 05 - Abu Dhabi - United Arab Emirates

13Views

Application Open:

Full-Time

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.

This is a Preview Listing…

You must sign in to see the full job description, and to apply.

Manage / Upgrade this job to a Full Job Listing.

Find Your Best Opportunity

Tell them AcademicJobs.com sent you!

Job Fulfilled

Related Jobs - August 22, 2026

View All Jobs

Essential

Related Conferences

University of the Arts SharjahUnited Arab Emirates

ATypI 2026 Sharjah

Oct 28, 2026 – Oct 31, 2026Sharjah, United Arab Emirates

To Be AnnouncedUnited Arab Emirates

IEEE World Forum on Internet of Things 2026

Oct 26, 2026 – Oct 29, 2026Abu Dhabi, United Arab Emirates

View all conferences →

Data Platform Engineer – IAAI

Applications Close: Aug 17, 2026

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

Residential Building (Biofuel Block) - Masdar City - SE45 05 - Abu Dhabi - United Arab Emirates

13Views

Application Open:

Full-Time

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.