Director - Data Engineering

We are looking for an experienced director - data platform engineering - to lead the design, development, and operations of scalable, cloud-native data platform capabilities. This role will drive engineering excellence, platform reliability, and the evolution of a modern AI-ready data ecosystem supporting multiple business domains and geographies. The ideal candidate will combine strong technical depth in modern data platforms with proven leadership experience in managing high-performing engineering teams and delivering enterprise-scale distributed data systems.

Responsibilities:

• Lead and grow a high-performing engineering team focused on scalable and reliable data platform capabilities.
• Own end-to-end delivery of platform components from architecture and development to deployment and operations.
• Partner closely with product managers and cross-functional stakeholders to translate business requirements into scalable technical solutions.
• Drive implementation of modern target-state data architecture aligned with enterprise standards and best practices.
• Establish and enforce engineering excellence across data engineering, DevOps, platform reliability, and operational processes.
• Ensure systems are designed for scalability, observability, resilience, performance, and cost efficiency.
• Collaborate with domain teams and regional business units to enable reusable data products and standardised platform capabilities.
• Identify and address systemic engineering challenges with a focus on long-term platform sustainability and maintainability.
• Evaluate emerging technologies and lead proofs of concept for platform innovation and modernisation initiatives.
• Foster a strong engineering culture emphasising ownership, accountability, continuous improvement, and operational excellence.

Requirements:

• 14+ years of overall experience with at least 8+ years in data engineering, data platforms, or distributed data systems.
• Proven experience building and operating enterprise-scale cloud-native data platforms.
• Strong experience working in globally distributed and federated engineering environments.

Cloud and Data Platform Expertise and Fault Tolerance:

• Strong hands-on expertise with Google Cloud Platform (GCP), including BigQuery (data warehousing, optimisation, partitioning, clustering, and cost management); Dataflow / Apache Beam for batch and streaming pipelines; Pub/Sub for event-driven and real-time architectures; Cloud Composer / Airflow for orchestration and workflow management; and Cloud Storage (GCS) within lakehouse or staging architectures.
• Deep understanding of modern data architectures and lakehouse patterns, distributed processing systems, Large-scale ingestion, transformation, and serving pipelines, Event-driven and streaming architectures, Fault-tolerant distributed systems.

Programming and Engineering:

• Strong programming expertise in Python, Spark / Scala, and distributed data application development.
• Experience implementing CI/CD for data pipelines, including version control, automated testing, and deployment automation.
• Strong understanding of data modelling, analytical workload optimisation, data quality frameworks, monitoring, lineage, and observability practices.

Data Product and AI Platform Experience:

• Experience with data product concepts, including: Data contracts, schema governance, discoverability and reusability, and ownership and lifecycle management.
• Exposure to MLOps and AI-enabled data platforms, including: Feature engineering pipelines, model training and inference workflows, and integration with Vertex AI or similar ML platforms.

Cost and Performance Optimisation:

• Experience managing cost-performance trade-offs in cloud data platforms, including query optimisation, resource scaling, streaming optimisation, and cloud cost governance.

Leadership and Collaboration:

• Proven track record of leading and mentoring high-performing engineering teams.
• Strong stakeholder management and cross-functional collaboration skills.
• Ability to influence teams across matrixed and multi-country environments without direct authority.
• Excellent communication skills with the ability to explain complex technical concepts to senior leadership and non-technical stakeholders.
• Strong product and platform mindset with a focus on scalable, reusable engineering solutions.
• Passion for innovation, operational excellence, and continuous improvement.

Preferred Qualifications:

• Experience working with enterprise-scale cloud-native data ecosystems.
• Exposure to data mesh and modern decentralised data platform concepts.
• Understanding of AI/ML platform integration and intelligent data systems.
• Experience driving engineering transformation and platform modernisation initiatives.
• A bachelor's or master's degree in computer science, engineering, or a related technical field.