Manager, Platform Operations

Job summary: The Manager, Data Platform is a senior technical and strategic leader who owns the Azure data platform roadmap, leads the data engineering function, governs data modelling standards, and ensures the platform delivers scalable, reliable, high-quality, and governed data pipelines and storage environments that serve TAQA's analytical, AI, and operational reporting needs. This role requires both deep Azure data engineering expertise and the strategic mindset to define platform direction, influence data investment decisions, and partner with the VP, Data Management and the Manager, Data and AI Governance to build a cohesive, governed, and high-performing data platform that positions TAQA Distribution at the forefront of data-led utility management in the UAE. Job Specific Responsibilities: Azure Data Platform Strategy & Architecture Ownership • Define and own TAQA Distribution's Azure data platform strategy and technical roadmap — evaluating emerging technologies (Microsoft Fabric, Delta Lake, Apache Iceberg, Databricks Lakehouse Platform), assessing their strategic relevance to TAQA's analytical and AI ambitions, and building a phased modernization roadmap that is aligned with the VP, Data Management's data strategy and TAQA's broader digital transformation agenda. • Architect and govern TAQA's Azure Lakehouse platform — designing the multi-zone Medallion architecture (Bronze/Silver/Gold layers) on Azure Data Lake Storage Gen2 (ADLS), Azure Databricks, and Azure Synapse Analytics — ensuring the platform is structured for scalability, data quality progression, reusability, and governed analytical consumption across all data domains. • Govern data platform architecture decisions — ensuring platform designs are scalable, cloud cost-optimized, secure, aligned with TAQA's enterprise architecture principles and data governance standards, and capable of evolving to support growing AI/ML, real-time analytics, and operational data integration workloads. Data Engineering Leadership & Pipeline Excellence • Lead TAQA's data engineering function — directing the design, development, testing, and production operation of enterprise-scale ETL/ELT data pipelines using Azure Data Factory (ADF) for orchestration and Azure Databricks (PySpark, Spark SQL, Delta Live Tables) for distributed data processing — ingesting data from TAQA's operational systems including Oracle Fusion, Oracle CC&B, Maximo, HES/MDMS, SAP, and IoT/sensor data sources, into the Azure data platform accurately and reliably. • Implement DataOps engineering standards across the data platform team — including Infrastructure-as-Code (Terraform or Azure Bicep) for platform provisioning, CI/CD pipelines for data pipeline deployments via Azure DevOps, automated data quality testing frameworks (dbt tests, Great Expectations), and Git-based version control — ensuring data engineering delivery is reproducible, auditable, and operationally excellent. • Govern data pipeline performance, reliability, and monitoring — implementing Azure Monitor, Databricks observability tooling, and ADF monitoring dashboards to ensure proactive detection and rapid resolution of pipeline failures, data quality exceptions, and performance degradation — maintaining high platform availability and data freshness SLAs for TAQA's analytical consumers. Data Modelling & Semantic Layer Governance • Define and enforce data modelling standards across TAQA's Azure data platform — governing dimensional modelling (star and snowflake schemas) for analytical workloads, Data Vault 2.0 structures for enterprise data warehouse flexibility and historical auditability, and semantic layer design using dbt, Azure Analysis Services, or Power BI datasets — ensuring data models are well-documented, performant, governed, and aligned with TAQA's key data domains (operational, customer, financial, workforce, and asset management). • Oversee utility domain data modelling for TAQA's operational data — including asset management (Maximo), metering and billing (Oracle CC&B/C2M), GIS network data, field service, SCADA/grid operations, and customer data — ensuring data structures in the Azure data platform accurately represent TAQA's operational reality and support reliable business decision-making. AI/ML Enablement & Advanced Analytics Infrastructure • Design and govern the Azure data platform infrastructure that underpins TAQA's AI and ML capabilities — including feature store architecture on Azure Databricks, ML training data pipeline design, MLflow integration for experiment tracking and model registry, and the provisioning of governed, high-quality AI training datasets — ensuring the data platform is a reliable foundation for TAQA's AI-powered analytics, predictive maintenance, and operational intelligence use cases. • Partner with TAQA's Digital Technology and AI/ML engineering teams to ensure the data platform provides the data infrastructure, feature engineering capabilities, and model serving integrations required to deploy AI models at scale across TAQA's operational and customer domains. Cloud Cost Governance, Data Governance & Stakeholder Value • Govern Azure data platform costs through active FinOps disciplines — including Databricks cluster right-sizing, auto-termination policies, Azure Synapse cost management, ADLS storage tier management, and Azure resource tagging for cost allocation by data domain and business unit — ensuring TAQA's data platform is operated cost-efficiently and cloud expenditure is transparently governed and reported to the VP, Data Management. • Partner closely with the Manager, Data and AI Governance to embed data governance into the data platform engineering workflow — ensuring data quality checks, metadata cataloguing, data lineage tracking, access control, and data privacy controls are built into the platform as foundational engineering standards rather than retrospective additions. • Operate as a strategic data platform partner to TAQA's analytics, AI, and business intelligence teams — translating analytical requirements into platform architecture decisions, ensuring the platform consistently delivers reliable, high-quality data assets, and providing VP, Data Management with regular reporting on platform performance, data quality KPIs, cloud cost optimization, and roadmap progress. Team Leadership & Data Engineering Capability Development • Lead, develop, and inspire a high-performing data platform engineering team — including the Lead, Data Platform; Associate, Data Engineering; and Sr. Associate, Utility Data Modeler — building deep Azure Databricks and data engineering expertise, a DataOps engineering culture, and a team that operates with the rigour, creativity, and strategic awareness of a world-class data engineering function. • Evaluate and adopt emerging Azure data platform capabilities — including Microsoft Fabric, Delta Lake open table format, Databricks AI/BI, and Azure OpenAI Service integrations — ensuring TAQA's data platform evolves continuously and positions the organization to leverage next-generation data and AI capabilities a