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 alti-global.com.
While professional experience and qualifications are key for this role, make sure to check you have the preferable soft skills before applying if required.
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.
In this hands-on leadership role, you will work at the intersection of data engineering, business intelligence, data science, strategy and governance. The ideal candidate will combine deep technical expertise in cloud data platforms and integration tools with strong experience implementing scalable data pipelines, robust data models, data visualization platforms and governance frameworks. This is a pivotal role in AlTi’s shift toward becoming a data-driven organization, with significant influence over our platform architecture, data quality standards, and analytics solutions. It will partner closely with both technology teams and business stakeholders.
Job Responsibilities
- Develop and lead a high-performing global data engineering team, championing excellence in data timeliness, integrity, infrastructure scalability, and operational efficiency.
- Lead the design, development, and support of scalable data pipelines and architectures that support applications, business intelligence and data science to assist with decision making in our advisory wealth, investment, corporate and operations functions.
- 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.
- Lead the integration of data across disparate systems using iPaaS platforms to ensure timely and accurate data flow across key business platforms including Addepar, NetSuite, Salesforce, and other external and internal applications.
- 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 planning and user acceptance testing.
- Work in close partnership with the wealth technology, information security, corporate technology, infrastructure teams and business management teams to ensure architectural alignment, shared services integration, and holistic platform delivery.
- Track progress against goals across owned workstreams and team deliverables, proactively identify and resolve blockers risks, and dependencies, and communicate updates to stakeholders in a clear and actionable manner.
- Support testing, rollout, adoption and change management activities across all initiatives
Qualifications
- 12+ years of technical hands-on experience in data engineering, data integration, or data architecture roles, including at least three years in a leadership position.
- Proven ability to lead and develop high-performing data teams, with a strong emphasis on professional growth, mentorship, retention, and creating a culture of continuous learning and technical excellence.
- Financial services experiences, ideally within wealth or asset management and associated data sets and applications.
- Proven experience designing and implementing cloud-native data platforms supporting analytics, business intelligence, and data science workloads including tools like and Microsoft Power BI, Tableau and Plotly.
- Strong hands-on experience with iPaaS platforms (e.g., Workato, Celigo, Boomi, MuleSoft), particularly in mid-market enterprise integration scenarios.
- Deep experience with the design, development, implementation and support of cloud-native data platforms such as Snowflake, Azure SQL Database, Databricks, Microsoft Fabric or Azure Synapse Analytics.
- Demonstrated success implementing data governance programs with tools like Collibra, Alation, Microsoft Purview, or Informatica, including projects around lineage, cataloging, and quality rules.
- Strong hands-on development experience in SQL and Python, with working knowledge of Spark or other distributed data processing frameworks.
- Design, development and implementation of distributed data solutions using API and microservice-based architecture.
- Deep understanding of ETL/ELT architecture, streaming, and event-driven processing; familiarity with tools like dbt, Airflow, Kafka, or equivalents.
- Familiarity with mid-sized firm tech stacks, especially in financial services, including systems such as NetSuite, Salesforce, Addepar,
- Experience with Atlassian Jira or Microsoft DevOps and associated development, CI/CD and release control frameworks.
- Experience supporting data science and analytics teams with curated datasets, feature engineering, and model deployment infrastructure.
- Knowledge of regulatory and security requirements around data in financial services, including GDPR, data retention, encryption, and access control.
- Excellent communication and collaboration skills with a strong ability to translate technical concepts into business value.
- Track record of success delivering outcomes in both waterfall and agile environments with distributed teams across time zones.
**NOTE: This role could be in our Lisbon or London offices
We’re building something meaningful at AlTi—and we’re looking for those who want to help shape it. If you're excited by the opportunity to work across a dynamic global platform and influence enterprise-wide technology transformation, we’d love to hear from you.
