Head of Data Engineering and Advanced Analytics

Overview In this role you lead BIS’s Data Engineering and Advanced Analytics AI teams within Banking Technology to shape data architecture and deliver data products. You ensure data quality, lineage, and reliable production services across data platforms and analytics apps. You translate business priorities into a multi-year data and analytics strategy while managing risk, governance, and operations. You join a globally oriented, international environment with hybrid work and relocation support, contributing to BIS’s data-driven initiatives and financial technology leadership. Leistungen / Benefits relocation support for candidate and dependents hybrid working options competitive compensation and benefits package childcare and education allowances where applicable international work environment Verantwortungsbereiche Lead Data Engineering and Advanced Analytics ensure resilience and performance Establish engineering standards for pipelines, datasets, metadata, observability and data quality Drive Data Governance improvements aligned with BIS policies and balance legacy systems with strategic progress Lead development, deployment and operation of analytics apps using quantitative finance, ML and generative AI with lifecycle governance Oversee delivery, security, controls and incident management; manage external partners and ensure cost efficiency Zentrale Anforderungen University degree or equivalent; ideally Master’s level in computer science, mathematics, statistics, quantitative finance or related discipline Strong people leadership for multidisciplinary teams Extensive experience delivering data, analytics and data engineering solutions Ability to define and execute a coherent data and analytics strategy and sustain production services Solid understanding of data engineering, analytics engineering and modern data-platform concepts (distributed processing, quality, lineage, metadata, security, observability) Knowledge of quantitative analytics, data science, ML and production model lifecycle Portfolio management skills: prioritisation, dependencies, investment planning, multi-year delivery Ability to communicate complex topics to management, users, engineers and control functions Relevant professional qualifications in data, cloud/platform engineering, ML, financial risk or technology management advantageous Leadership and people management Strategic thinking and cross-functional collaboration Clear communication with senior stakeholders Data engineering and data platforms Advanced analytics and AI (ML, generative AI) Data governance, data quality, lineage and observability