SVP Technology Architect / Development Leader – Data & Analytics, GMET

UOB•Phoenix, AZ

14d

About The Position

About UOB United Overseas Bank Limited (UOB) is a leading bank in Asia with a global network of more than 500 branches and offices in 19 countries and territories in Asia Pacific, Europe and North America. In Asia, we operate through our head office in Singapore and banking subsidiaries in China, Indonesia, Malaysia and Thailand, as well as branches and offices. Our history spans more than 80 years. Over this time, we have been guided by our values – Honorable, Enterprising, United and Committed. This means we always strive to do what is right, build for the future, work as one team and pursue long-term success. It is how we work, consistently, be it towards the company, our colleagues or our customers. Job Description We are seeking an experienced senior Technology Leader to design, build, and manage data and analytics solutions for the banking domain. This role spans data architecture, analytics application development, and AI/ML application integration — ensuring scalable, secure, and regulatory-compliant platforms that deliver business value. The ideal candidate combines deep technical expertise in data engineering and analytics platforms with the ability to lead teams and partner with business stakeholders.

Requirements

Proven experience in architecting and delivering enterprise-scale data & analytics platforms for banking.
Areas of interest are: Data Platforms: Teradata, Hadoop/Cloudera, Snowflake, Databricks, Azure, Google, Huawei , Alicloud.
BI & Analytics Platforms: SAS Viya, Teradata, Adobe, Cloudera, Microsoft Power BI / Azure Analytics, Qlik.
AI/ML, Analytics: Spark Scala, Python, R, Java, TensorFlow, PyTorch, Data Ops tooling, MLflow, Kubernetes.
Streaming & Integration: Kafka, Spark Streaming, Informatica, Talend.
Databases, Data federation: SQL, NoSQL DBs, Denodo, Snowflake.
Cloud & DevOps: Kubernetes, Airflow, GitOps, CI/CD tools.
Knowledge of statistical modeling, predictive analytics, NLP, and AI-driven decision systems.
Platforms such as FICO, Experian, SAS for credit and portfolio management
Financial services data models & data modelling tools and data governance processes.
Hadoop languages & tools – Spark, Python, R, Pig, Hue, Impala, Hive, Hbase, Informatica IDL
Platforms such as Kubernetes or OpenShift for deploying container-based workload.
Experience with AI in banking regulatory contexts (model governance, explainability, audit readiness).
Exposure to data mesh, federated governance, and advanced BI/AI architectures.
10+ years in data/analytics technology, with 5+ years in an architectural or leadership role.
Demonstrated ability to lead multi-disciplinary teams (engineering, BI, data science).
Strong grasp of banking operations, risk, compliance, fraud, and customer analytics.
Excellent communication skills to influence C-level executives and business stakeholders.
Bachelor’s degree/University degree in Computer Science, Engineering, or equivalent experience.
Certifications: TOGAF, DAMA/DMBOK, CDMP, SAS Certified AI/ML Specialist, Microsoft Data & AI, or equivalent.
More than 15 years track record in developing and delivering global/regional IT capabilities for a multi-national/regional company with annual budgetary responsibility.
More than 5 years leadership experience in managing IT delivery teams
Experience in implementing large-scale project implementation
Proven result-oriented person with a focus on delivery.
Experience in actual systems development work, with prior coding, functional specifications and technical specifications and systems development and testing experience (in aggregate no less than 8 years).
Good understanding and experience in software development cycle
Highly effective communicating with technical stakeholders, proficient communicating with non-technical stakeholders.
Good problem solving, analytical, synthesis, system thinking and solutioning skills.
Functional knowledge of Retail bank, Wholesale, Global Markets, Finance, Risk and Compliance oriented products & business processes.

Responsibilities

Contribute to enterprise data architecture strategy and regulatory needs.
Architect end-to-end data platforms supporting traditional BI, advanced analytics, AI/ML, and real-time insights.
Define data analytics solution adapting concepts like Lakehouse, Data Mesh, Data Fabric while adopting cloud-native data & AI services and modern integration patterns.
Work on banking-grade platforms including Opensource technologies, Teradata, Cloudera, SAS Viya, Adobe, IBM DataPack, Microsoft Power BI and other enterprise data analytics tools.
Build and maintain enterprise data platforms leveraging open-source technologies ranging data integration, data base, visualisation, modelling, apis/micro services.
Lead design and delivery of analytical applications leveraging enterprise Data Analytics platforms (dashboards, predictive models, reporting suites, self-service and operational analytics).
Build and operationalize Data products, AI/ML solutions for banking use cases such as next best offer, fraud detection, customer segmentation, credit scoring, and risk modelling etc.
Architect and develop data pipelines, APIs, and data serving layers for production-grade analytics.
Deploy best practices in Data Ops, ML Ops and integration with regulatory-compliant systems.
Define enterprise design principles, security and control standards.
Plan technical deliverables (including any system enhancements and upgrades) to meet project’s requirements within allocated budget and schedule.
Plan & collaborate across different application teams to manage technical dependencies of the solution.
Provide status update related to technical delivery to Project Manager (PM)
Partner with System Analysts and Business Solution Specialist to collate, understand and finalize functional and technical requirements.
Partner with test Manager for delivery through SIT, UAT , performance / load testing and application security testing with quality results.
Manage technical implementation plan across application teams - coordinate technical implementation activities across application teams to ensure non-event production cutover and adequate post implementation support.
Escalate issues that impacts project schedule on timely basis and propose workarounds/resolutions.
Lead and mentor cross-functional teams of data engineers, BI developers, data scientists, and ML engineers.
Establish engineering standards, DevOps/MLOps pipelines, and governance for data analytics applications.
Collaborate closely with business leaders, ensuring technical solutions meet business goals and compliance standards.