Lead, Data Platform

Job summary:

To lead the technical design, development, and maintenance of TAQA Distribution's data platform, ensuring data pipelines, data models, and data storage environments are built to high engineering standards and deliver reliable, high-quality data for analytics, AI/ML, and reporting. The Lead, Data Platform acts as the senior data engineering authority within the data platform team, working under the direction of the Manager, Data Platform to deliver scalable data infrastructure and guiding the Associate, Data Engineering and Sr. Associate, Utility Data Modeler in their technical work.

General Responsibilities:

• Policy, Procedures, Process and Systems
• Ensure the compliance of organization’s policy, systems, processes, procedures, and controls in line with group delegation of authority policy so that all relevant procedural/legislative requirements are fulfilled while delivering a quality, cost-effective service in a consistent manner.
• Reporting
• Ensure that all reports are completed timely and comply with the business policies and standards.
• Manage the preparation of periodical management reports and progress reports to keep the business senior management informed about the progress of various initiatives and to facilitate decision-making.
• Comply with organization’s requirements in a timely manner.

Job Specific Responsibilities:

• Lead the design, development, and maintenance of TAQA's data platform infrastructure, including data lake storage (Azure ADLS), data warehouse/lakehouse compute ( Azure Synapse, Databricks, or equivalent), and data ingestion frameworks.
• Design, build, and optimise complex data pipelines and ETL/ELT workflows using modern data engineering tools (Apache Spark, dbt, Azure Data Factory, or equivalent), ensuring data flows reliably from source systems to the data platform.
• Lead data modelling activities for the data platform, designing dimensional models, data vault structures, and semantic layer definitions that support accurate and performant analytical consumption.
• Implement data platform best practices including infrastructure-as-code (Terraform, CloudFormation), CI/CD for data pipelines, data testing frameworks, and data lineage tracking.
• Monitor data platform performance and data pipeline health, proactively identifying and resolving pipeline failures, data quality issues, and performance bottlenecks.
• Provide technical guidance and mentoring to the Associate, Data Engineering and Sr. Associate, Utility Data Modeler, reviewing code, setting engineering standards, and building team capability.
• Collaborate with the data governance team to ensure data platform outputs comply with data quality standards, metadata requirements, and data privacy controls.
• Apply AI-powered data engineering tools, automated pipeline testing, and intelligent data quality monitoring to improve platform reliability and engineering productivity

HSE, Security, and Risk Protocols:

• The Employee shall adhere to all the HSE, Security and Risk Management Rules & Procedures communicated by the company, including:
• Taking reasonable care of their own health, safety, and security.
• Taking reasonable care of the health, safety and security of persons that may be affected by their acts or omissions at work.
• Co-operate with their employer with respect to any instruction and/or actions taken by the employer to protect the employee and/ or comply with HSE and security requirements.
• Report to their immediate line manager any situation which they have reason to believe could present a risk, hazard, or issue to an individual or the company and which they cannot correct themselves.
• Report all HSE and security incidents and work-related injuries.
• Not intentionally or recklessly interfere with or misuse anything provided at the workplace that supports the interest of HSE, security and welfare.

Detailed HSE Roles & Responsibilities for staff are identified in the dedicated company procedure.

People Management Responsibilities:

• Leadership
• Actively participate in continuous improvement and professional development activities. Support decisions made with integrity and transparency, always aligning with the entity's goals.
• Talent Management
• Engage in talent development programs aimed at enhancing skills and supporting career progression. Contribute to a team culture that is connected to the organization's larger purpose.
• Culture
• Uphold and promote the organization's values within the team. Foster a collaborative and innovative work environment through active participation and support.
• Communication
• Support informed decision-making within the team. Contribute to clear and effective communication, ensuring alignment with organizational objectives and facilitating smooth information flow up and down the chain.

Financial Dimension

Non-Financial Dimension

Competencies

Proficiency Level

Technical Competencies:

• Data Pipeline Development (Apache Spark, dbt, Azure Data Factory)
• Data Lake & Data Warehouse/Lakehouse Architecture ( Azure ADLS/Synapse, Databricks)
• Data Modelling (Dimensional, Data Vault, Semantic Layer)
• DataOps & CI/CD for Data Pipelines (Terraform, GitHub Actions)
• Data Quality Monitoring & Lineage Tracking
• AI & Data Engineering Automation Tool Proficiency

Strategic & Business Competencies:

• Data Platform Architecture Alignment
• Data Governance & Quality Partnership
• Stakeholder Communication & Technical Documentation
• Continuous Improvement & Engineering Excellence

1. •Advanced to Expert in Technical competencies

• •Intermediate to Advance in business competencies

Profile

Essential Requirements

• Bachelor’s degree in Information Technology or equivalent
• 6 years of relevant working experience

Desired Requirements

• Master’s degree in Information Technology or equivalent
• 9 years of relevant working experience