Lead AI Data Engineer, Wealth Management

Job Summary

• As a Lead AI Data Engineer, your responsibilities include designing, building, and maintaining scalable data infrastructure and pipelines that enable data-driven decision making across the organization. You will work closely with data scientists, analysts, business stakeholders, and development teams to ensure reliable, efficient, and secure data flow from various sources to analytics, reporting platforms, and AI-powered applications.
• You will also support AI agent use cases by engineering the data foundations, retrieval pipelines, vector and knowledge store integrations, governance controls, and observability needed for agentic systems to operate accurately, securely, and at scale.
• Ultimately, you will be hands on working directly with cross-functional teams to architect and implement robust data solutions that support business intelligence, machine learning models, operational reporting needs, and emerging AI agent capabilities such as domain-specific assistants, workflow automation, and decision-support systems.

Key Responsibilities

• Application Data Strategy and Leadership - Lead the design and evolution of application-level data layers that support business applications and AI agent use cases, ensuring each business function is served through a consistent, governed, and scalable data path.
• Golden Source Integration Standards - Define and drive data integration patterns that prioritize authoritative upstream sources, reduce unnecessary duplication across platforms, and align delivery teams to approved golden-source principles.
• Application Data Solution Design - Shape fit-for-purpose data stores and access patterns for applications, including Databricks-based serving layers where justified, with clear decisions around latency, reliability, resiliency, and operational supportability.
• Data Quality, Governance, and Observability - Establish and enforce data quality controls, lineage, governed access, monitoring, and troubleshooting practices so application data layers remain trusted, auditable, and resilient.
• AI Agent and LLM Enablement - Lead and contribute hands-on to the design and delivery of AI agent and LLM-based solutions through reusable tools, secure enterprise data connectivity, prompt and retrieval orchestration, guardrails, evaluation, and scalable Python-based integration patterns.
• Cross-Functional Delivery and Alignment - Partner with application teams, architects, data owners, and AI engineers to define source-to-consumption flows, keep business logic outside the core data layer, and align implementation across teams to a coherent data strategy.
• Innovation and Technology Direction - Evaluate and recommend emerging technologies and practices that strengthen application data engineering and AI enablement, including Databricks-native AI capabilities, vector search, MCP/tool integration approaches, and agent frameworks.

Strategy
• Supports Hive Tech Lead, Product Area Lead and Engineering in strategy of data infrastructure and roadmap execution.
• Lead, driv, and deliver data solutions that make a significant contribution to the organization's strategic goals and enable data-driven decision making.
• Contribute to data platform strategy and modernization initiatives across the organization.

Business
• Promote engineering craftsmanship and focus on delivering business value through reliable and efficient data solutions.
• Responsible for technical delivery and technical excellence of data pipelines and infrastructure across squads.
• Works across multiple squads or hives for delivery of complex data integration projects, Features, Epics & Releases.
• Point of escalation for data pipeline issues and delivery challenges across all impacted hives and squads.
• Work with Change Delivery Manager (Release Manager) to develop delivery and release plans for data projects with technical dependencies, aligning all partners with support from the team and architect.
• Exhibit passion for data quality and reliability with a user-first mindset for data consumers.
• Drive the domain towards future state of banking and technology through modern data practices.
• Ensure the technical consistency of data solutions with the business architecture, technology architecture and enterprise technology standards.

Processes
• Study existing data engineering processes and contribute to improvements to drive up efficiencies in data delivery and operations.
• Analyse, remove, or mitigate data dependencies across teams to enable speed and quality of delivery.
• Responsible for the preparation of production data outputs and their planning in alignment to ESDLC standards.
• Implement DataOps practices to streamline data pipeline development, testing, and deployment.

People & Talent
• Work on upskilling technical expertise of Engineers to enable them to become more efficient in data engineering practices and tools.
• Mentoring and training the squad on data engineering best practices, technologies, and methodologies.
• Share knowledge on data modelling, pipeline design patterns, and performance optimization techniques.

Risk Management
• Follows the standards with respect to risk management as applicable to their chapter domain.
• Adheres to common practices to mitigate risk in their respective domain.

Governance
• Ensure all artefacts and assurance deliverables are as per the required standards and policies (e.g., SCB Governance Standards, ESDLC etc.).

Regulatory & Business Conduct
• Display exemplary conduct and live by the Group’s Values and Code of Conduct.
• Take personal responsibility for embedding the highest standards of ethics, including regulatory and business conduct, across Standard Chartered Bank. This includes understanding and ensuring compliance with, in letter and spirit, all applicable laws, regulations, guidelines and the Group Code of Conduct.
• Effectively and collaboratively identify, escalate, mitigate and resolve risk, conduct and compliance matters.

Key stakeholders
• Architect, Engineering Leads.
• Global Wealth Management Digital Owners, AI Capability Chapter, AI Factory
• Business & Technology Managers, for solution design and delivery.

Other Responsibilities
• Embed Here for good and Group’s brand and values in Wealth Journey squad;
• Perform other responsibilities assigned under Group, Country, Business or Functional policies and procedures

Skills and Experience

• Software Product Business Knowledge
• Strong understanding of Wealth Journey and WM Sales Governance processes (onboarding New to Wealth customers, risk profiling, pre/post trade compliance check, portfolio performance and etc)
• Software Product Technical Knowledge
• Strong understanding of Wealth Journey Platform architecture, functionality, and capabilities, with hands-on experience in solution design, configuration, customisation, and integration.
• Databases – Oracle, MS SQL, PostgreSQL, Vector DB
• Container Platform – OCP, AKS, EKS
• Agile Methodologies
• Software Change Request Management
• Application Delivery Process
• Technical Troubleshooting

Preferred Qualifications (Nice to Have)

• Previous experience in the Financial Technology (Wealth Management, Asset Management) sector.
• Familiarity with financial concepts like portfolio modelling, rebalancing, risk profiling, and compliance regulations (e.g., FINRA, SEC).
• Experience with observability tools (Prometheus, Grafana, ELK stack), and/or monitoring patterns for Databricks workloads, AI agents, LLM calls, and tool execution telemetry.
• Experience building domain-oriented AI or analytics solutions for Wealth Advisory, client servicing, investment research, or advisor enablement use cases.
• Familiarity with Databricks ML/AI capabilities, vector search, model serving, and integration patterns for enterprise LLM platforms.

Qualifications

Education• Bachelor's degree, or higher, in computer science, Software Engineering, Information Technology, or a related field (or equivalent experience)

Required Qualifications & Skills
• Strong programming skills in Python, Java, and SQL, including hands-on PySpark development and Python-based integration patterns for LLM and agent applications.
• Strong understanding of data modeling, ETL/ELT, Apache Spark, Kafka, Delta Lake, and retrieval-oriented data design, including feature and embedding pipelines.
• Experience with cloud data platforms (AWS, Azure, GCP) and relevant data services, with strong preference for Databricks Lakehouse, Unity Catalog, Databricks Workflows, and platform-native AI capabilities.
• Hands-on experience with orchestration tools (such as Airflow) for data pipelines, model and agent workflows, and tool-enabled processing jobs across environments.
• Strong knowledge of relational and NoSQL databases, data lakes, and data warehousing, plus familiarity with vector databases and vector search for LLM and agent architectures.
• Experience implementing data quality controls, governance, lineage, security best practices, and compliance requirements.
• Excellent analytical and problem-solving skills, with the ability to troubleshoot complex data pipeline issues, distributed Spark workloads, and LLM or tool integration failures.
• Ability to collaborate effectively with cross-functional teams, including data scientists, analysts, business stakeholders, AI engineers, platform teams, architects, and product leads.
• Experience with Git and CI/CD practices for data pipelines, including deployment automation for Databricks assets, Python services, agent components, and environment configuration.
• Overall 15+ years of engineering experience building data-driven applications, including proven tenure in senior or lead engineering roles, with demonstrated delivery of enterprise-scale data platforms or AI-enabled data products in cloud environments.
• Hands-on experience building or integrating AI agents, MCP-compatible tools, reusable skills, and Python services, with practical understanding of prompt orchestration, tool calling, retrieval-augmented generation, evaluation, observability, and guardrails for production LLM applications.

Long Description

Preferred Qualifications (Nice to Have)
• Previous experience in the Financial Technology (Wealth Management, Asset Management) sector.
• Familiarity with financial concepts like portfolio modelling, rebalancing, risk profiling, and compliance regulations (e.g., FINRA, SEC).
• Experience with observability tools (Prometheus, Grafana, ELK stack), and/or monitoring patterns for Databricks workloads, AI agents, LLM calls, and tool execution telemetry.
• Experience building domain-oriented AI or analytics solutions for Wealth Advisory, client servicing, investment research, or advisor enablement use cases.
• Familiarity with Databricks ML/AI capabilities, vector search, model serving, and integration patterns for enterprise LLM platforms.

About Standard Chartered

We're an international bank, nimble enough to act, big enough for impact. For more than 170 years, we've worked to make a positive difference for our clients, communities, and each other. We question the status quo, love a challenge and enjoy finding new opportunities to grow and do better than before. If you're looking for a career with purpose and you want to work for a bank making a difference, we want to hear from you. You can count on us to celebrate your unique talents and we can't wait to see the talents you can bring us.

Our purpose, to drive commerce and prosperity through our unique diversity, together with our brand promise, to be here for good are achieved by how we each live our valued behaviours. When you work with us, you'll see how we value difference and advocate inclusion.

Together we:

• Do the right thing and are assertive, challenge one another, and live with integrity, while putting the client at the heart of what we do
• Never settle, continuously striving to improve and innovate, keeping things simple and learning from doing well, and not so well
• Are better together, we can be ourselves, be inclusive, see more good in others, and work collectively to build for the long term

What we offer

In line with our Fair Pay Charter, we offer a competitive salary and benefits to support your mental, physical, financial and social wellbeing.

• Core bank funding for retirement savings, medical and life insurance, with flexible and voluntary benefits available in some locations.
• Time-off including annual leave, parental/maternity (20 weeks), sabbatical (12 months maximum) and volunteering leave (3 days), along with minimum global standards for annual and public holiday, which is combined to 30 days minimum.
• Flexible working options based around home and office locations, with flexible working patterns.
• Proactive wellbeing support through Unmind, a market-leading digital wellbeing platform, development courses for resilience and other human skills, global Employee Assistance Programme, sick leave, mental health first-aiders and all sorts of self-help toolkits
• A continuous learning culture to support your growth, with opportunities to reskill and upskill and access to physical, virtual and digital learning.
• Being part of an inclusive and values driven organisation, one that embraces and celebrates our unique diversity, across our teams, business functions and geographies - everyone feels respected and can realise their full potential.