Top Data Jobs in Singapore

The Senior Network Engineer leads global network operations, large-scale projects, and process improvements, ensuring network performance, reliability, and security. They collaborate on network design, manage migrations, and maintain infrastructure in a hands-on role covering multiple global sites.

Conduct data analysis using SQL and Python, automate processes, collaborate with teams, and assist with UAT migration tasks.

The role involves managing KYC processes, coordinating transaction execution tasks, maintaining databases, and providing administrative support to the Capital Markets team.

Senior individual contributor leading global commercial risk and insurance programs. Performs risk identification, advanced analysis, mitigation strategy design, insurance placements and claims oversight. Partners with Legal, Finance, Operations and external brokers, mentors junior staff, delivers training, and supports crisis response and enterprise risk decision-making.

As a Data Engineer at SHOPLINE, you'll be responsible for managing payments data, enhancing data quality, and constructing business evaluation metrics while collaborating across functions.

The Cloud Client Partner at NTT DATA will drive managed services and outsourcing sales by building client relationships, generating demand for cloud solutions, and collaborating with internal teams to close deals.

Design and govern enterprise network solutions for large Singapore accounts. Lead presales activities (proposals, RFPs, POCs), produce technical specifications, guide sales, and transition solutions to delivery while providing hands-on technical leadership.

The Senior Sales Specialist identifies and pursues leads, establishes client relationships, and collaborates with internal teams to achieve sales targets through solution-based selling.

Responsible for managing and expanding enterprise accounts, driving revenue growth through strategic partnerships, client engagement, and deal closure. Requires influencing senior stakeholders and executing complex sales cycles.

A structured graduate programme delivering technical training, mentorship, certifications and hands-on client project experience across cloud, data, AI/ML, application development, automation, infrastructure and cybersecurity to prepare graduates for specialised technology career paths.

The role involves managing day-to-day operations of Zscaler services, troubleshooting incidents, policy administration, and integration management while ensuring secure and reliable user connectivity.

The Client Partner role involves generating demand for cloud services, building client relationships, managing sales processes, and constructing managed services deals while collaborating with internal teams and partners.

As a Senior Associate Client Manager, you will manage client relationships, drive expansion and renewals, and ensure satisfaction through effective communication and sales strategies using Salesforce.com and other tools.

Senior hands-on technical architect for pre-sales and early adoption, designing and operationalizing enterprise data and AI architectures around Salesforce Agentforce and Data Cloud (D360). Lead prototypes, prompt engineering, agent interoperability, integrations with hyperscalers and data platforms, enablement, and governance to drive scalable, secure AI-enabled CRM solutions.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Lead advancement of group data management and governance: implement and monitor data quality rules, manage data lineage/catalogue, translate business requirements into ETL/DQ rules, redesign DQ framework for enterprise platform, automate catalogue and reporting, perform data analysis using SQL/Python and big-data tools, support testing, and partner with auditors and regulators to ensure compliance.

Designs and implements a scalable hybrid cloud data platform on GCP to process petabytes of data, support ML workloads, automate infrastructure provisioning (Terraform), operate containers (Kubernetes/Docker), monitor and secure systems, and resolve L2/L3 operational issues using CI/CD and TDD practices.

Lead single-threaded product and technical ownership for Client Intelligence & Data, defining APIs and event-driven architectures to deliver merchant-facing analytics, KPIs, and reports. Drive platform strategy toward AI-driven insights, enforce production reliability and automated QA, champion You Build It You Run It culture, partner cross-functionally to scale multi-tenant analytics capabilities, and prioritize work using evidence-based frameworks.

Lead network capacity planning and analysis across a large global infrastructure. Analyze traffic and performance metrics, forecast growth, recommend hardware upgrades, produce capacity reports and KPIs, mitigate congestion risks, and collaborate with network, demand management, and project teams to align capacity plans with business goals.

Develop machine learning and statistical models for geotechnical laboratory and project datasets; automate QA/QC and anomaly detection; integrate and clean LIMS/LabPro/RFID data; link laboratory, CPT, geophysical and imaging data; produce dashboards and analytics for operations; validate models with domain experts and document methods for the Marine Ground Digital Twin platform.

Design, build and maintain scalable ETL/ELT data pipelines and integrations on Microsoft Fabric and Databricks. Prepare and model data for AI/ML and analytics, ensure data quality, lineage, security and governance, develop connectors/APIs, support CI/CD deployments, monitor pipelines, and document handovers while collaborating with stakeholders and AI/ML engineers.

Lead social media and external communications strategy: consult with partners, craft and project corporate messaging across social channels, oversee analysis of social responses and recommend actions, represent the company to online communities and media, and hire/develop team members to run the function efficiently.

Use large datasets to produce dashboards, reports and visualizations; identify trends and anomalies; perform statistical analysis and predictive modeling; collaborate with business units to translate needs into specifications and resolve data quality issues; communicate findings to non-technical stakeholders.

Lead end-to-end data science initiatives across pay TV, digital products, media sales, and customer lifecycle. Design, deploy, monitor ML/AI solutions (churn, propensity, personalization, recommendation, forecasting), partner with stakeholders, build data pipelines, run experiments (A/B, uplift), mentor teams, and translate analytics into measurable commercial impact.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.