{"id":1252116,"url":"https://alion.io/job/standard-chartered-lead-ai-data-engineer-wealth-management","title":"Lead AI Data Engineer, Wealth Management","company":{"id":20709,"name":"Standard Chartered","domain":"sc.com","url":"https://alion.io/company/standard-chartered-gbs","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":{"grade":"A","score":88,"open_postings":37,"ghost_share":0,"stale_share":0.676,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Guangzhou, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":37000,"max_usd":83000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":709},"experience_years_min":15,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Azure AKS","optional":false},{"name":"Databricks","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"MS SQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Airflow","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"Delta Lake","optional":true},{"name":"ETL/ELT","optional":true},{"name":"Function Calling","optional":true},{"name":"GCP","optional":true},{"name":"Git","optional":true},{"name":"Grafana","optional":true},{"name":"Java","optional":true},{"name":"Kubernetes","optional":true},{"name":"Prometheus","optional":true},{"name":"pySpark","optional":true},{"name":"RAG","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-25T18:36:02Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-24T16:43:28Z","board_verified":true,"closed_at":null,"days_open":29,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":29},"description":"Job Summary\nAs 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.\nYou 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.\nUltimately, 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.\nKey Responsibilities\nApplication 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.\nGolden 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.\nApplication 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.\nData 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.\nAI 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.\nCross-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.\nInnovation 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.\n\nStrategy\nSupports Hive Tech Lead, Product Area Lead and Engineering in strategy of data infrastructure and roadmap execution.\nLead, driv, and deliver data solutions that make a significant contribution to the organization's strategic goals and enable data-driven decision making.\nContribute to data platform strategy and modernization initiatives across the organization.\nBusiness\nPromote engineering craftsmanship and focus on delivering business value through reliable and efficient data solutions.\nResponsible for technical delivery and technical excellence of data pipelines and infrastructure across squads.\nWorks across multiple squads or hives for delivery of complex data integration projects, Features, Epics & Releases.\nPoint of escalation for data pipeline issues and delivery challenges across all impacted hives and squads.\nWork 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.\nExhibit passion for data quality and reliability with a user-first mindset for data consumers.\nDrive the domain towards future state of banking and technology through modern data practices.\nEnsure the technical consistency of data solutions with the business architecture, technology architecture and enterprise technology standards.\nProcesses\nStudy existing data engineering processes and contribute to improvements to drive up efficiencies in data delivery and operations.\nAnalyse, remove, or mitigate data dependencies across teams to enable speed and quality of delivery.\nResponsible for the preparation of production data outputs and their planning in alignment to ESDLC standards.\nImplement DataOps practices to streamline data pipeline development, testing, and deployment.\n\nPeople & Talent\nWork on upskilling technical expertise of Engineers to enable them to become more efficient in data engineering practices and tools.\nMentoring and training the squad on data engineering best practices, technologies, and methodologies.\nShare knowledge on data modelling, pipeline design patterns, and performance optimization techniques.\nRisk Management\nFollows the standards with respect to risk management as applicable to their chapter domain.\nAdheres to common practices to mitigate risk in their respective domain.\nGovernance\nEnsure all artefacts and assurance deliverables are as per the required standards and policies (e.g., SCB Governance Standards, ESDLC etc.).\nRegulatory & Business Conduct\nDisplay exemplary conduct and live by the Group’s Values and Code of Conduct.\nTake 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.\nEffectively and collaboratively identify, escalate, mitigate and resolve risk, conduct and compliance matters.\nKey stakeholders\nArchitect, Engineering Leads.\nGlobal Wealth Management Digital Owners, AI Capability Chapter, AI Factory\nBusiness & Technology Managers, for solution design and delivery.\nOther Responsibilities\nEmbed Here for good and Group’s brand and values in Wealth Journey squad;\nPerform other responsibilities assigned under Group, Country, Business or Functional policies and procedures\nSkills and Experience\nSoftware Product Business Knowledge\nStrong 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)\nSoftware Product Technical Knowledge\nStrong understanding of Wealth Journey Platform architecture, functionality, and capabilities, with hands-on experience in solution design, configuration, customisation, and integration.\nDatabases - Oracle, MS SQL, PostgreSQL, Vector DB\nContainer Platform - OCP, AKS, EKS\nAgile Methodologies\nSoftware Change Request Management\nApplication Delivery Process\nTechnical Troubleshooting\nPreferred Qualifications (Nice to Have)\nPrevious experience in the Financial Technology (Wealth Management, Asset Management) sector.\nFamiliarity with financial concepts like portfolio modelling, rebalancing, risk profiling, and compliance regulations (e.g., FINRA, SEC).\nExperience with observability tools (Prometheus, Grafana, ELK stack), and/or monitoring patterns for Databricks workloads, AI agents, LLM calls, and tool execution telemetry.\nExperience building domain-oriented AI or analytics solutions for Wealth Advisory, client servicing, investment research, or advisor enablement use cases.\nFamiliarity with Databricks ML/AI capabilities, vector search, model serving, and integration patterns for enterprise LLM platforms.\nQualifications\nEducation\n\nBachelor's degree, or higher, in computer science, Software Engineering, Information Technology, or a related field (or equivalent experience)\nRequired Qualifications & Skills\nStrong programming skills in Python, Java, and SQL, including hands-on PySpark development and Python-based integration patterns for LLM and agent applications.\nStrong understanding of data modeling, ETL/ELT, Apache Spark, Kafka, Delta Lake, and retrieval-oriented data design, including feature and embedding pipelines.\nExperience 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.\nHands-on experience with orchestration tools (such as Airflow) for data pipelines, model and agent workflows, and tool-enabled processing jobs across environments.\nStrong knowledge of relational and NoSQL databases, data lakes, and data warehousing, plus familiarity with vector databases and vector search for LLM and agent architectures.\nExperience implementing data quality controls, governance, lineage, security best practices, and compliance requirements.\nExcellent analytical and problem-solving skills, with the ability to troubleshoot complex data pipeline issues, distributed Spark workloads, and LLM or tool integration failures.\nAbility to collaborate effectively with cross-functional teams, including data scientists, analysts, business stakeholders, AI engineers, platform teams, architects, and product leads.\nExperience with Git and CI/CD practices for data pipelines, including deployment automation for Databricks assets, Python services, agent components, and environment configuration.\nOverall 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.\nHands-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.\nLong Description\nPreferred Qualifications (Nice to Have)\nPrevious experience in the Financial Technology (Wealth Management, Asset Management) sector.\nFamiliarity with financial concepts like portfolio modelling, rebalancing, risk profiling, and compliance regulations (e.g., FINRA, SEC).\nExperience with observability tools (Prometheus, Grafana, ELK stack), and/or monitoring patterns for Databricks workloads, AI agents, LLM calls, and tool execution telemetry.\nExperience building domain-oriented AI or analytics solutions for Wealth Advisory, client servicing, investment research, or advisor enablement use cases.\nFamiliarity with Databricks ML/AI capabilities, vector search, model serving, and integration patterns for enterprise LLM platforms.\nAbout Standard Chartered\nWe'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.\nOur 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.\nTogether we:\nDo the right thing and are assertive, challenge one another, and live with integrity, while putting the client at the heart of what we do\nNever settle, continuously striving to improve and innovate, keeping things simple and learning from doing well, and not so well\nAre better together, we can be ourselves, be inclusive, see more good in others, and work collectively to build for the long term\nWhat we...","description_format":"text","description_chars":13260,"description_truncated":true,"requirements":{"experience_years_min":15,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Annual leave","Continuous learning","Flexible schedule","Life insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Wealth Management & Financial Advisors","Commercial & Retail Banks","Investment Banking & M&A Advisory"],"lifecycle":[{"event":"open","at":"2026-09-25T18:36:02Z"}],"visa":[],"liveness":{"score":23,"band":"cold","label":"Long shot","p_open":1,"p_active":0.506,"p_room":0.45,"age_days":28,"expected_fill_days":18,"reasons":["conf:8","stale_co","velocity","win:tail","comp:brand"],"computed_at":"2026-10-24T06:02:35Z"},"pay":null,"html_url":"https://alion.io/job/standard-chartered-lead-ai-data-engineer-wealth-management","json_url":"https://alion.io/job/standard-chartered-lead-ai-data-engineer-wealth-management.json","meta":{"generated_at":"2026-10-25T02:13:46Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":3370,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-26T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":20709},"rest":"https://alion.io/mcp/rest/get_company?id=20709"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fstandard-chartered-lead-ai-data-engineer-wealth-management"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fstandard-chartered-lead-ai-data-engineer-wealth-management"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fstandard-chartered-lead-ai-data-engineer-wealth-management"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/standard-chartered-lead-ai-data-engineer-wealth-management\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fstandard-chartered-lead-ai-data-engineer-wealth-management"}]}