Data Architect Lead

The Data Architect Lead is responsible for leading and influencing advanced data initiatives for AD Airports. The role focuses on managing, architecting, and analysing large-scale data environments to generate data-driven insights and develop high-impact data models. The role holder builds a comprehensive data value chain covering acquisition, evaluation, transformation, and analysis of data from multiple sources. By delivering strategic insights, the position supports competitive advantage, organizational assets development, and thought leadership. The role also ensures alignment between data architecture strategy and business objectives while advancing data governance and enterprise data standards.

Responsibilities

• Manage the full lifecycle of big data solutions, including:
  • Platform selection
  • Architecture and application design
  • Requirements analysis
  • Testing and deployment
• Design and evolve the analytics technology roadmap aligned with changing business needs.
• Identify and evaluate emerging tools, capabilities, and trends for future adoption.
• Define best practices, guidelines, and ensure integration with enterprise solutions.
• Establish governance frameworks ensuring compliance with:
  • Security standards
  • Auditability requirements
  • Data governance policies
  • Metadata management standards
• Build and contribute to data streaming platforms.
• Provide technical oversight to solution delivery teams, ensuring adherence to enterprise architecture and governance standards.
• Contribute to the evolution and adoption of Data Governance practices across the airport ecosystem.
• Explore and evaluate new data sources for product value creation.
• Design platforms to improve:
  • Data quality
  • Metadata management
  • Data governance
  • Data-driven decision-making
• Define best practices for data ingestion and transformation.
• Collaborate with data engineers to:
  • Ingest, transform, and provision data
  • Support digital analytics
  • Enable real-time event sourcing
  • Support AI model execution
• Develop principles ensuring enterprise-wide data quality and integrity across:
  • Core systems
  • Commercial functions
  • Airport Operations
  • Customer 360
  • BI and Data Warehouse
  • External interfaces
• Align Data Architecture strategy and roadmap with overall business and technology strategies.
• Develop short-term solutions and long-term data roadmaps for management.
• Define procedures for data identification, collection, validation, and corporate data improvement.
• Implement and oversee solutions related to:
  • Metadata management
  • ETL processes
  • Data Quality
  • Data Lakes
  • Big Data
  • Advanced analytics
  • Data streaming
  • Data visualization
• Map enterprise data entities to business capabilities and applications.
Maintain horizontal data lineage from source to output.

Requirements and Skills

Education & Qualifications

• Bachelor of Engineering (mandatory).
• Master’s degree in Computer Science or Computer Applications (preferred).
• Data Architecture certification in Azure.
• 9+ years of total experience, with at least 6 years in a relevant data architecture field.

Technical Experience

• Experience with Big Data platforms and distributed computing such as:
  • Azure
  • Azure Data Lake
  • Azure Synapse
• Experience in:
  • Data Quality Assessment (profiling, anomaly detection)
  • Data documentation (schemas, data dictionaries)
  • Data architecture and data warehousing
  • Data modelling techniques (Relational, ETL/ELT)
  • Azure Data Factory (ADF)
• Experience designing architectures leveraging cloud data platforms (Azure).
• Experience architecting hybrid data solutions (cloud and on-premises environments).
• Strong understanding of:
  • RDBMS
  • NoSQL databases
  • Cache and in-memory stores
  • Data streaming platforms

Core Competencies

• Excellent communication and stakeholder management skills.
• Strong analytical and problem-solving abilities.
• Expertise in analysing large and complex datasets to extract actionable insights.
• Advanced knowledge of statistics and machine learning.
• Ability to align data initiatives with organizational goals and create strategic value chains.
• Strong understanding of data governance frameworks, privacy regulations, and quality assurance practices.
• Ability to collaborate with government stakeholders and translate complex data concepts into strategic recommendations.
• Capability to develop data-related frameworks, assets, and best practices.
• Strong numerical and analytical skills for interpreting large volumes of data.