The Data Engineering & Architecture Associate Director leads the organization's data engineering efforts, driving the development and optimization of data infrastructure and integration platforms. This role is critical in ensuring that data assets are effectively leveraged to support strategic decision-making, operational efficiency, and innovation across the company. The position involves charting the course for the data engineering landscape, ensuring that the infrastructure meets current demands while remaining adaptable to future technological advancements and evolving business needs.

In addition to engineering leadership, the role has a strong architectural focus. The Data Engineering & Architecture Associate Director is responsible for defining and maintaining the enterprise data architecture, ensuring it supports scalable, secure, and interoperable data solutions. This includes setting architectural standards, guiding the design of data platforms, and ensuring alignment with business and IT strategies. The individual will serve as the Lead Data Architect on major data programs, providing technical direction and ensuring architectural integrity across initiatives.

Working closely with leaders across the organization, the Data Engineering & Architecture Associate Director ensures that data infrastructure and architecture align with broader organizational goals and contribute meaningfully to the company’s digital transformation journey.

Roles & Responsibilities:

• Architect and oversee the development of scalable, high-availability data infrastructure using the Microsoft Azure stack, including Azure Data Factory, Azure Event Hubs, Databricks, and Microsoft Fabric.
• Spearhead the optimization of data pipelines using advanced ETL frameworks and data streaming technologies, ensuring efficient data flow and storage for real-time and batch processing.
• Implement automation and monitoring tools to ensure high performance and operational efficiency.
• Lead the design, development, and maintenance of complex data systems and solutions across the enterprise.
• Develop and maintain conceptual, logical, and physical data models to support analytics, BI, AI, and operational needs.
• Create and maintain target-state data architectures, ensuring alignment with group standards and strategic direction.
• Simplify existing data architecture by delivering reusable services and identifying cost-saving opportunities.
• Act as Lead Data Architect on major programs, translating high-level business requirements into robust data models and standards.
• Govern and shape the delivery of large-scale data-centric projects, ensuring compliance with enterprise architecture and data governance.
• Collaborate with external suppliers and technology partners to ensure solutions leverage best-in-class tools and meet business needs.
• Define and manage standards, guidelines, and processes to ensure data quality, consistency, and compliance.
• Ensure data engineering practices adhere to governance policies and data protection regulations.
• Work closely with data science, analytics, IT, and business teams to understand data needs and deliver comprehensive solutions.
• Oversee end-to-end data lifecycle management activities, from ingestion to archival.
• Participate in supplier selection processes, evaluating technologies and making recommendations based on strategic fit and cost-effectiveness.
• Evaluate and recommend emerging technologies for data management, storage, and analytics, with a focus on cloud-native solutions and modern data platforms.
• Lead the exploration and adoption of innovative data engineering methodologies and tools, including Microsoft Fabric, Azure Synapse Analytics, Databricks, and Power BI integration.

Credentials and Experience:

• Master’s or bachelor’s degree in business, computer science, computer engineering, electrical engineering, system analysis or a related field of study, or equivalent experience
• 10+ years of experience in data engineering, with at least 3 years in a leadership role overseeing data engineering teams.
• Demonstrated experience designing and implementing large-scale data processing systems and architectures.
• Proven track record of managing complex data projects and delivering tangible outcomes.

Skills:

• Strong knowledge in cloud data platforms like Microsoft Azure Data Factory, Azure Event Hubs, Databricks, Microsoft Fabric, Informatica IDMC, Confluent Kafka, Looker, Google Pub/Sub, and Google Dataflow.
• Mastery in designing and implementing complex data pipelines, with extensive experience in ETL/ELT processes, data lakes, data warehousing, and batch/real-time data streaming architectures.
• Expertise in optimizing data processing performance and efficiency, including tuning of data storage, database indexing, and query optimization.
• Ability to diagnose and resolve performance bottlenecks in data pipelines and storage systems.
• Proven leadership and team management experience in a data engineering context.
• Strong strategic planning and execution skills.
• Exceptional technical proficiency in data processing technologies, data modeling, and architecture principles.
• Excellent communication and collaboration skills, with the ability to engage effectively with both technical and non-technical stakeholders.
• Strong problem-solving and analytical thinking abilities.
• Deep knowledge of data architecture and modeling principles, including data warehousing, ETL processes, and real-time data processing frameworks.
• Comprehensive knowledge of data architecture principles and patterns, including data lakes, data warehouses, and data marts. Understanding of the architectural considerations for processing large-scale data systems, including data partitioning, indexing, and optimization strategies.
• Profound understanding of both SQL and NoSQL database management systems, including relational databases (PostgreSQL, Oracle, MySQL)
• Knowledge of database design, transaction management, and performance tuning.
• Understanding of data governance principles, data quality management, data lifecycle management and regulatory compliance rules and regulations such as GDPR, CCPA, PDPL or any other UAE laws.
• Awareness of current trends and emerging technologies in data engineering and the broader data ecosystem.