JOB DESCRIPTION
Job Summery:
The Head of Data Management & Engineering will lead the data management, data product, and engineering capabilities supporting Commercial & Investment Bank (CIB) Marketing. As a member of the CIB Marketing Data & Analytics leadership team, you will define and execute the data strategy required to enable scalable analytics, effective marketing decisions, and responsible adoption of artificial intelligence. The role combines technical leadership, data product ownership, governance, and stakeholder management. You will lead teams responsible for data pipelines, architecture, curated datasets, and reusable data products supporting marketing measurement, client intelligence, lead generation, campaign analytics, digital analytics, and advanced analytical solutions. You will also partner with leaders across Marketing, Analytics, Data Science, Product, Technology, Operations, and Risk and Control.
CIB Marketing supports multiple lines of business and sub-lines of business within a complex B2B environment. This leader must translate varied business needs into a coherent data roadmap, establish common solutions where scale is possible, and accommodate legitimate business-specific requirements where necessary.
Job responsibilities:
Define and execute the data management and engineering strategy for CIB Marketing, aligning technical investments and delivery priorities with business, marketing, and analytical objectives.
Lead and develop teams responsible for data engineering / architecture, data integration, AI/ML modeling, and data products.
Own data and controls for global email audience targeting and list management, including segmentation/suppression logic, opt-out and preference management, and integration of sanctions screening (e.g., OFAC and other restricted-party lists) into campaign workflows.
Establish and manage a multi-year roadmap for the data capabilities supporting marketing measurement, lead generation, client intelligence, campaign and digital analytics, reporting, and AI/ML use cases.
Translate business and analytical requirements into scalable technical solutions, including source integration, data modeling, transformation logic, reconciliation, quality controls, metadata, and consumption patterns.
Build reliable, well-governed data products that combine information from multiple sources and can be used across lines of business, analytical teams, and use cases.
Establish effective data product and portfolio-management disciplines, including consumer discovery, prioritization, roadmaps, release planning, service expectations, and adoption measurement.
Ensure appropriate governance across the data lifecycle, including data quality, metadata, lineage, access, privacy, retention, control evidence, and compliance with enterprise standards.
Build strong relationships with senior business, analytics, product, technology, and control leaders. Communicate technical trade-offs, delivery risks, dependencies, and investment needs clearly and persuasively.
Recruit, develop, and retain high-performing technical talent while fostering a culture of accountability, collaboration, innovation, and production-ready delivery.
Required qualifications, capabilities, and skills:
Extensive experience in data engineering, data architecture, data management, software engineering, or a related discipline, including significant leadership of technical teams and complex delivery portfolios.
Strong technical knowledge of modern data architecture and engineering practices, including data integration, ETL/ELT, data modeling, APIs, orchestration, cloud or distributed data platforms, SQL, and software-development lifecycle practices.
Demonstrated ability to translate business and analytical needs into scalable technical solutions, reusable data products, and executable roadmaps.
Experience leading data engineers, architects, technical product owners, or multidisciplinary technology teams.
Strong understanding of data governance and controls, including data quality, metadata, lineage, privacy, security, resiliency, and auditability.
Proven ability to influence senior business and technology stakeholders, resolve competing priorities, and communicate complex technical issues to nontechnical audiences.
Practical understanding of AI-assisted engineering and data-management workflows, including the evaluation, validation, and responsible use of AI-generated outputs.
Bachelor's degree in computer science, engineering, information systems, data science, or a related discipline, or equivalent professional experience.
Preferred qualifications, capabilities and skills:
Experience supporting marketing, marketing analytics, CRM, lead management, client intelligence, digital analytics, or product and customer data.
Experience within commercial banking, investment banking, payments, markets, financial services, or another complex B2B environment.
Experience building data products for advanced analytics, machine learning, or AI use cases.
Experience with platforms and tools such as Databricks, Spark, Python, SQL, cloud data services, orchestration tools, data catalogs, and data-quality platforms.
