Lead, Big Data Analytics & Engineering

Data Engineering & Platform Leadership
• Lead the ingestion, transformation, aggregation, and processing of large scale datasets to enable advanced analytics and downstream consumption.
• Design, build, and maintain robust, scalable data pipelines across Hadoop and enterprise data platforms, ensuring high standards of data quality, reliability, performance, and availability.
• Drive data unification initiatives, integrating multiple structured and semi structured data sources into a cohesive, governed analytical foundation.
Advanced Analytics Enablement
• Manipulate and analyse high volume, high velocity, and high dimensional datasets using modern big data frameworks.
• Analyse large volumes of transactional and product data to produce insights and actionable recommendations that support business growth and value realisation.
• Apply metrics, measurement frameworks, and benchmarking techniques to evaluate solution effectiveness and drive continuous improvement.
Cross Functional Collaboration
• Partner with Product Managers, Data Science, Platform Strategy, and Technology teams to understand analytical and data requirements and translate them into scalable engineering solutions.
• Act as a technical bridge between business, analytical, and engineering teams, clearly articulating architecture decisions, trade offs, and implementation approaches.
• Enable alignment across stakeholders to ensure data solutions are directly tied to business and customer outcomes.
Innovation & Value Creation
• Identify innovation opportunities and deliver proofs of concept, prototypes, and pilot solutions aligned to near term and future business needs.
• Integrate new and emerging data assets that enhance existing platforms, products, and services, strengthening overall value propositions.
• Gather and synthesise feedback from clients, product, engineering, and sales teams to inform new solutions and product enhancements.
Technical Leadership & Mentorship
• Provide technical leadership, guidance, and mentorship to data engineers and analysts, setting standards for engineering quality, scalability, performance, and maintainability.
• Promote best practices in data modelling, pipeline design, performance optimisation, and data governance.
• Influence engineering standards, architectural consistency, and long term platform sustainability.