Job description
About the Role
The Data Engineer (AI/ML) builds and operates data pipelines, feature stores, and data infrastructure that enable AI use cases across Zain Group. This role serves as the critical bridge between OpCo data systems, Group Data CoE, and AI delivery teams - ensuring clean, accessible, high-quality data is available for AI model training, inference, and analytics. The Data Engineer coordinates data access across 6 OpCos, assesses and remediates data quality issues, implements data governance for AI, and supports GenAI engineers with data for RAG pipelines. This role requires both technical data engineering skills and the stakeholder management capability to navigate complex data ownership and access politics across a federated organization.
Key Accountabilities
Build and operate data pipelines for AI use cases including ETL/ELT from OpCo BSS/OSS and enterprise systems
Create and maintain feature stores for ML models ensuring data quality, lineage, and governance
Coordinate data access with OpCo IT teams, Group Data CoE, and data governance functions
Assess data quality, completeness, and fitness-for-purpose for AI initiatives and drive remediation
Implement data governance controls for AI including privacy, consent, retention, and audit requirements
Support GenAI/RAG engineers with data preparation, ingestion, and optimization for vector databases
Design and implement data architectures for real-time and batch AI workloads
Monitor data pipeline health, performance, and SLAs with proactive issue resolution
Document data assets, schemas, and lineage in data catalog for discoverability and reuse
Partner with OpCo data engineers on data quality improvements and standardization initiatives
KPIs can include but are not limited to:
Data availability: percentage of required data sources accessible and pipeline-ready for AI use cases
Data quality: data quality scores (completeness, accuracy, timeliness) meeting AI requirements
Pipeline reliability: uptime and performance of data pipelines meeting defined SLAs
Time-to-data: average time from data requirement to production-ready data pipeline
Stakeholder satisfaction: OpCo and AI team satisfaction with data engineering support
What We Need From You
Educational Qualification: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related technical field.
Experience: 5-8 years in data engineering with 2+ years focused on AI/ML data pipelines. Hands-on experience with feature stores, data quality, and large-scale data processing. Experience in telecom domain with BSS/OSS data systems highly valuable.
Knowledge: Strong expertise in data engineering tools and platforms (Spark, Airflow, Kafka, cloud data platforms). Experience with feature stores, data quality frameworks, and ML data pipelines. Understanding of data governance, privacy, and compliance. SQL and Python proficiency. Knowledge of vector databases and embeddings for GenAI advantageous.