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AI & Data Center of Excellence – Abu Dhabi, UAEAs a Data Engineer, you will be responsible for building and maintaining scalable, reliable, and secure data platforms that power analytics and AI use cases across the organization.This role is critical in enabling data-driven decision-making in financial services environments. You will work closely with data scientists, AI engineers, and business stakeholders to ensure data systems are robust, performant, and aligned with regulatory and operational requirements.Experience Bands• Senior Data Engineer: 8–10 years of experience• Data Engineer: 5–7 years of experienceKey ResponsibilitiesDesign and implement robust ETL/ELT pipelines for structured and unstructured dataBuild and manage scalable data lakes, data warehouses, and real-time data pipelinesEnsure data quality, lineage, governance, and compliance across data platformsEnable reliable data availability for analytics, reporting, and AI systemsOptimize data infrastructure for performance, scalability, and cost efficiencyCollaborate with Data Science and AI teams to productionize machine learning pipelinesMonitor and troubleshoot data workflows and system performanceImplement best practices for data security and reliabilityFinancial Services Use Cases (Preferred)Candidates with experience in financial data environments will be highly valued, particularly in:Transaction data pipeline development and managementRegulatory reporting and compliance data systemsRisk and finance data martsCustomer 360 and customer analytics platformsTechnical SkillsData Platforms• Snowflake• BigQuery• Amazon Redshift• DatabricksData Processing Technologies• Apache Spark• Apache Kafka• Apache FlinkDatabases• SQL databases• NoSQL databasesDevOps & Engineering Practices• CI/CD pipelines• Version control systems (e.g., Git)Containers & Infrastructure• Docker• KubernetesCloud Platforms• AWS• Azure• Google Cloud Platform (GCP)Evaluation CriteriaCandidates will be evaluated based on:Complexity and scale of data systems built and maintainedReliability and performance of data pipelines in production environmentsExperience implementing data governance and compliance standardsExposure to AI and machine learning data pipelinesAbility to design scalable and resilient data architecturesKey Performance Indicators (KPIs)Reliability of data pipelines (uptime, failure rate)Data latency and freshnessData quality and integrity metricsCost optimization and efficiency of data infrastructureStability and scalability of data platformsPreferred ProfileExperience working within financial data ecosystemsUnderstanding of regulatory data requirements and compliance standardsExposure to MLOps and machine learning data pipelinesExperience working in distributed or cross-functional teamsStrong problem-solving and ownership mindset
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