{"id":1998314,"url":"https://alion.io/job/dbs-vp-full-stack-engineering-lead-equity-wealth-management-technology-group-technology","title":"VP, Full Stack Engineering Lead (Equity), Wealth Management Technology, Group Technology","company":{"id":451141,"name":"DBS","domain":"dbs.com","url":"https://alion.io/company/dbs-4","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":76,"open_postings":275,"ghost_share":0,"stale_share":0.975,"repost_share":0,"time_to_fill_p50_days":22,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Backend","role_family":"Backend","seniority":"head","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":58000,"max_usd":139000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":130},"experience_years_min":9,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CI/CD","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"NLP","optional":false},{"name":"OpenShift","optional":false},{"name":"Python","optional":false},{"name":"Spring Boot","optional":false},{"name":"WebSockets","optional":false}],"status":"live","first_seen_at":"2026-10-07T10:04:45Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-16T19:15:58Z","board_verified":true,"closed_at":null,"days_open":10,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":10},"description":"ABOUT THE ROLE\nAs the VP, Full Stack Engineering Lead for Equity Products and Execution, you will drive the technical vision, architectural strategy, and delivery execution for a next-generation Cash Equity and Equity Derivatives engineering ecosystem. This mission-critical mandate will align equity product lines and build connected employee and client journeys through robust capabilities, shared platform services, and high-performance execution workflows across Cash Equities, Equity Swaps, Listed Options, Delta-One, and Structured Equity products.\nWe are looking for a strategic, hands-on engineering leader who combines deep equity domain knowledge with expertise in scalable, cloud-native, high-throughput full-stack platforms. You will evolve the equity execution platform by identifying shared architectural patterns and translating complex multi-asset transactional and analytical requirements into a resilient, real-time distributed ecosystem.\nWHAT YOU’LL DO\nArchitectural Leadership & Evolution: Drive the technical vision and evolution of the equity execution ecosystem into a cloud-native, microservices-based architecture of shared capabilities on modern container platforms such as OpenShift or Kubernetes.\nEquity Product Alignment: Identify architectural commonalities across Cash Equities, Equity Swaps, Listed Derivatives, and Structured Equity products, designing reusable services that address shared execution, pricing, and platform needs.\nHands-on Technical Execution: Lead, mentor, and guide engineering squads while driving common engineering standards, reusable code patterns, and unified design reviews across equity product lines.\nHigh-Throughput Streaming & Messaging: Design and optimize low-latency equity transaction flows, real-time market data distribution, and event-driven architectures using WebSockets and modern messaging platforms such as AMQ or Kafka.\nFull-Stack Platform Delivery: Champion resilient server-side architectures using Java, Spring Boot, and Python, alongside interactive ReactJS frontends that support real-time operational workflows, equity risk views, and trade lifecycle flows.\nMarket Data & Analytics Integration: Orchestrate real-time equity market data ingestion from sources such as Bloomberg, Reuters, and exchange feeds, and integrate pricing models, volatility surfaces, and analytics engines into the equity transaction lifecycle.\nEcosystem Innovation: Evaluate and deploy emerging technologies-including AI coding assistants, NLP applications, and LLM integrations-to improve developer productivity and modernize the equity engineering ecosystem.\nStakeholder Partnership: Serve as a key technical advisor to product owners, business heads, and senior leadership across equity desks, translating business needs into cohesive technical roadmaps and execution plans.\nWHAT WE’RE LOOKING FOR\nProven Track Record: 9+ years of experience in enterprise financial environments such as Tier 1 investment banks or institutional FinTechs, with proven success leading Agile engineering squads focused on equity trading platforms.\n Full-Stack Technical Mastery: Expert-level, hands-on engineering capability across Java (Spring Boot), ReactJS, and Python.\n Cloud-Native & Container Depth: Practical experience architecting and migrating platforms to modern container orchestrators (such as Kubernetes or OpenShift), with strong CI/CD automation expertise.\nShared Services Mindset: Demonstrated ability to identify overlapping business and technical requirements across equity product desks and consolidate them into robust, reusable services.\n High-Availability Systems Design: Proven success designing and delivering high-performance, low-latency equity platforms such as electronic order management systems (OMS/EMS), algorithmic execution hubs, real-time risk platforms, or equivalent high-throughput streaming systems for equities.\nDeep Equity Domain Knowledge: Strong expertise across Cash Equities, Equity Swaps, Delta-One, and Listed/Exotic Equity Derivatives, with a clear understanding of trade lifecycles, STP, and electronic distribution.\nResilient Engineering Practices: Strong commitment to software craftsmanship, including TDD, automation, memory profiling, performance stress testing, and clean-code architectural patterns.\nInnovation-Driven: Active experience or strong interest in leveraging AI assistant coding tools and LLMs to optimize team delivery and software engineering lifecycles.\nAcademic & Professional Credentials: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical discipline. 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