Head of Data Engineering

**Company Description** AlTi Tiedemann Global ("AlTi") is a NASDAQ listed global wealth manager, creating possibility, impact and legacy for the most discerning and dynamic owners of capital in the world. The firm currently manages or advises on approximately $77 billion in combined assets and has an expansive network of c.400 professionals across three continents. Our work ranges from helping clients leave a lasting legacy or create meaningful impact in the world, to structuring a complex estate or investing in compelling alternatives. Whether our clients are individuals or institutions, foundations or multi-generational families, we offer a connected ecosystem of advice, solutions and investment opportunities from across our global network. We are passionate about finding better ways to serve our clients. We foster a firmwide culture of collaboration and an entrepreneurial approach. We believe these differences make us better suited for a fast-changing world. As a growing global firm with offices in 20 major financial centers, we are looking for talented individuals to expand our team. If you share our passion for ideas and commitment to excellence, we want you to join us. To learn more visit. **Job Description & Overview** The Head of Data Engineering & Analytics will lead the development and execution of AlTi's enterprise data engineering strategy, enabling the capture, transformation, storage and delivery of high-quality data across the firm's global wealth, investment, corporate and asset management functions. This leader will architect and scale data engineering capabilities to support real-time and batch integration, reporting, and advanced analytics. This role reports to the CTO and will be a key member of the Global Technology Solutions leadership team. **Responsibilities**: - Develop and lead a high-performing global data engineering team, championing excellence in data timeliness, integrity, infrastructure scalability, and operational efficiency. - Own the strategy, architecture, platform and solutions responsible for the end-to-end data acquisition, transformation, storage and delivery, including ETL/ELT, integration and cloud database solutions. - Manage cloud-based data infrastructure on platforms such as Azure, Amazon Web Services, or Google Cloud Platform, with focus on cost optimization, stability, scalability, and performance. - Collaborate with business analytics and data science teams to ensure data environments are optimized for downstream consumption, including modeling, visualization, and machine learning. - Champion the use of data analytics, reporting, and business intelligence tools to support decision-making, performance tracking, and regulatory needs across corporate functions. - Implement and maintain robust data models across key domains using best practices in dimensional modeling, normalization, and semantic layering. - Standardize data acquisition, onboarding, ingestion, transformation and distribution frameworks globally to optimize scalability, open architecture and delivery speed. - Support the implementation of data governance frameworks, partnering with internal stakeholders to design and implement tools for data lineage tracking, data quality monitoring, and metadata cataloguing. - Drive adoption of common standards for data access, tagging, and classification in alignment with regulatory compliance, risk, sovereignty and privacy obligations. - Ensure solutions adhere to internal governance standards, including information security, data privacy, compliance, and change control procedures - Design and manage cloud-based data platforms to support both transactional and analytical workloads, ensuring optimized performance for structured, unstructured and time-series data. - Implement storage and query strategies tailored to workload types-using row-based storage for high-frequency transactional operations and columnar formats for efficient large-scale analytical querying. - Support DevOps practices including CI/CD, infrastructure-as-code, automated testing, release and version control and system observability for data pipelines. - Establish metrics and KPIs and identify and deploy tools to measure data pipeline health, data quality, timeliness and accuracy, team performance, cost-effectiveness, and business impact. - Actively mentor and grow talent within the team while fostering a collaborative and outcome-driven culture. - Engage directly with technology and business stakeholders to gather requirements, identify pain points, and translate them into detailed user stories and functional specifications. - Manage data platform vendor relationships with procurement and oversee platform integration efforts, ensuring systems work cohesively within the broader business architecture and future state vision - Prioritize and refine the product backlog based on business value, risk, and technical feasibility, coordinating agile delivery activities including sprint plannin