HSBC
Sr. Associate Director, Software Engineering
- Compensation
- Not disclosed
- Location
- Guangzhou, Guangdong, China
- Work setup
- Onsite
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Proven ability to use AI effectively to solve real engineering problems (design, coding, testing, analysis, automation), with a pragmatic and outcomes-focused mindset.
- Strong problem-solving abilities, including diagnosing ambiguous issues and translating them into data/AI solutions that ship.
- Excellent communication skills (written and verbal), able to influence and enable engineers and leaders across teams.
- Strong experience building production-grade data pipelines and datasets, with attention to reliability, observability, and data quality.
- Proven ability to build dashboards/analytics products that drive action, not just reporting.
- Strong understanding of secure data handling and engineering practices appropriate for a regulated environment.
- Experience working in Agile delivery environments and collaborating across product, engineering, and risk/security stakeholders.
Full description
Some careers have more impact than others.
If you’re looking for a career where you can make a real impression, join HSBC and discover how valued you’ll be.
We are currently seeking an experienced professional to join our team in the role of Sr. Associate Director, Software Engineering.
Business: Digital Platforms
Principal responsibilities
Applied AI for Engineering Productivity (Design/Coding/Testing)
- Identify high-impact opportunities to use AI to improve mobile engineering outcomes across design, coding, testing, release readiness, and incident response.
- Build and operationalise AI-assisted workflows for engineers (e.g., code generation and refactoring support, test case generation, defect triage, documentation automation, release note drafting), ensuring strong governance and quality controls.
- Develop AI-powered accelerators and reusable components (e.g., prompt patterns, evaluators, automation scripts, agentic workflows) that teams can adopt with minimal friction.
- Establish practical guidance for “how we use AI here” for common engineering tasks—balancing speed with correctness, security, and maintainability.
Data Engineering & Mobile Data Products
- Design, build, and maintain reliable data pipelines and curated datasets using mobile-related data sources such as: app telemetry, crash logs, performance metrics, network traces, CI/CD signals, test execution, feature usage analytics, customer journeys, and support/incident data.
- Define data models that enable consistent, trusted reporting and AI use cases (e.g., quality and stability KPIs, release health, funnel analysis, defect leakage, performance regression tracking).
- Implement data quality checks, lineage, monitoring, and documentation so stakeholders can trust and reuse the data confidently.
- Collaborate with security and risk partners to ensure compliant data handling, access controls, and responsible use of customer and operational data.
Dashboards & Decision Intelligence (Data-Driven Mobile)
- Build dashboards and decision tools that make excellent use of mobile-related data—turning metrics into insights and insights into actions.
- Create “single pane of glass” views for mobile engineering health (stability, performance, delivery, quality, customer impact), with clear definitions and drill-down paths.
- Enable proactive management through leading indicators and alerting (e.g., early detection of regressions, risk signals before release, anomaly detection in crash/performance patterns).
- Partner with mobile leadership to define KPIs that matter, establish targets, and drive measurable improvement over time.