Top Data Jobs in Singapore

Analyze operational, financial, maintenance, and resident feedback data to identify trends and service gaps. Develop and maintain dashboards and KPIs, automate reporting, improve data governance, and provide actionable insights to operations and management. Prepare presentations and support ad-hoc studies to inform strategic planning and enhance resident experience.

Design and maintain data pipelines, build data warehouse models, optimize dbt Cloud workflows, improve CI/CD and DevOps practices, and support AI initiatives involving Data Genie. Collaborate with business stakeholders to resolve data issues, deliver actionable insights, and support operational needs across business units.

Leads mechanical engineering design delivery for hyperscale and co-location data center projects. Oversees technical quality, project objectives, schedules, resources, budgets, and multidisciplinary coordination. Builds client partnerships, supports bids and proposals, contributes to thought leadership, and drives sector growth. Requires extensive data center or mission-critical engineering experience, commercial acumen, professional engineering qualifications, Uptime Institute ATD certification, and eligibility for Baseline or higher AGSVA clearance.

Lead the APAC Solutions Consulting organization in a player-coach capacity. Recruit, mentor, and manage Solutions Architects; own technical strategy and complex customer solutions; partner with sales, professional services, product, and engineering; build performance processes; influence deal acceleration, technical win rates, and retention; and engage customers through executive sessions, workshops, and conferences.

Drive new enterprise business development and own the full sales cycle, from prospecting and discovery through negotiation and contract closure. Build qualified pipeline, manage Salesforce forecasts, develop account and opportunity strategies, engage C-suite stakeholders, position enterprise technology solutions, lead consultative negotiations, and coordinate internal teams and partners to deliver compelling proposals and profitable growth.

Lead data discovery, governance, and master data management for supply chain. Define processes, KPIs, and quality metrics; translate business needs into data requirements; resolve data issues; run workshops and collaborate across technical and business teams to improve data quality and implement solutions.

Leads analysis, mining, modeling, and visualization of large-scale transactional, operational, and customer data. Partners with compliance teams to prioritize analytical use cases, support regulatory requirements, improve business efficiency, and translate complex findings into actionable recommendations. Oversees multidisciplinary analysts, manages stakeholder data requests, assesses business changes, and develops analytical systems using machine learning, big data technologies, and visualization tools.

Leads VAST Data’s APJ pre-sales function, personally supporting complex AI infrastructure opportunities and executive customer engagements. Builds, coaches, and retains a distributed regional solutions engineering team; sets technical strategy; partners with sales, channels, alliances, product, and engineering; influences roadmap through customer and competitive insights; and represents VAST with partners, analysts, press, and the NVIDIA ecosystem. Requires extensive APAC travel and a Singapore base.

Analyzes sales channels, business performance, revenue, and acquisition trends; develops dashboards and reports; designs and calculates sales incentive programs; monitors targets; and presents actionable insights to stakeholders. The role collaborates with Sales, HR, and other business teams, supports strategic decision-making, and applies banking and wealth management industry knowledge to identify optimization and innovation opportunities.

Develop multilingual LLMs for Bahasa Indonesia and regional languages. Responsibilities include creating datasets for pretraining, fine-tuning, alignment, and evaluation; training and scaling models; implementing safety and guardrail systems; researching efficient methods; and collaborating with product and engineering teams to deploy reliable, production-grade AI solutions.

Own data and AI product initiatives end to end, from defining problems and data requirements through coding, evaluation, launch, monitoring, improvement, and adoption. Build production systems with AI coding agents, create release evaluation suites, track product and operational metrics, measure business impact, collaborate with technical and business teams, and document products through runbooks, design notes, evaluation reports, and training materials.

Support data transformation, structure, metadata, and workflow processes. Extract and analyze transaction, marketing, app usage, and financial data. Build interactive visualizations and reports by integrating KPIs from multiple sources, enabling business teams to self-serve insights and helping stakeholders answer questions about business performance.

Support and govern HR technology platforms (Workday and similar), maintain HR data integrity, manage system configuration and access controls, execute and monitor SOX/internal controls, support audits and UAT, perform data audits and reconciliations, identify control gaps and support remediation, and partner with HR, IT, Finance, Risk, and Internal Audit to ensure compliance and continuous improvement.

Lead cost management for complex construction projects from initiation through completion. Responsibilities include cost planning, estimating, reporting, forecasting, value engineering, lifecycle costing, budget and risk management, contract administration, invoice coordination, stakeholder engagement, team leadership, client management, and business development. The role oversees multidisciplinary teams, ensures financial and delivery controls, presents cost plans and reports, and develops cost managers while supporting client objectives and business growth.

Design and build scalable cloud-native data pipelines and lakehouse solutions using Spark, PySpark, Apache Iceberg, streaming technologies, Python, shell scripting, and SQL. Modernize legacy workloads, migrate systems to cloud architectures, implement data quality and monitoring automation, and support metadata, lineage, and governance through open catalogue frameworks. Collaborate with analysts, data scientists, and stakeholders to deliver reliable, high-performance data solutions.

Design and build cloud-native data pipelines, lakehouse solutions, and real-time streaming systems using Spark, PySpark, Iceberg, Python, and SQL. Modernize legacy workloads, migrate processing systems to cloud architectures, automate ETL/ELT workflows, and implement data quality, monitoring, governance, metadata, and lineage practices. Collaborate with analysts, data scientists, and stakeholders to deliver reliable, scalable data solutions and troubleshoot pipeline performance and operational issues.

Leads concurrent standard and complex client projects from conception through implementation and operational handoff. Manages scope, budgets, schedules, quality, risks, issues, change control, stakeholder communications, client satisfaction, and escalations. Coordinates project resources and supports presales activities, solution scoping, proposals, client presentations, and services positioning. Requires strong project management, commercial, communication, influencing, problem-solving, and client engagement skills.

The role involves leading BIO initiatives, collaborating with sales teams, enabling digital capabilities, and engaging stakeholders to enhance agent productivity and business outcomes.

Lead data engineering and process-mining initiatives to capture Digital Footprints, generate actionable visualizations and AI-driven optimizations, standardize scalable digital processes across Front-End sites, and track productivity and labor-hour savings via dashboards and cross-functional execution.

Develop end-to-end data science and ML solutions for semiconductor manufacturing to improve yield, quality, cycle time, and reduce cost. Work across the full pipeline from data acquisition and cleansing through model building, deployment, visualization, and collaboration with international engineering teams. Leverage statistical modeling, deep learning, generative AI techniques, and manufacturing domain knowledge to drive high-impact projects.

Design and deploy computer vision and deep learning solutions for manufacturing inspection and monitoring. Extract and clean large datasets, build multimodal AI models, apply MLOps for production deployment, and collaborate with cross-functional teams to translate research into operational systems and documentation.

Apply data science and ML to semiconductor yield and process optimization: build data pipelines, analyze sensor and tool logs, support DOE, develop models, and create dashboards to guide engineering improvements.

Design, build, and maintain scalable data pipelines, data models, feature stores, and data catalogues to support AI model training and inference. Ensure data quality, lineage, governance, and secure integration of diverse real-time and batch transport data sources. Collaborate with AI architects, infrastructure engineers, and stakeholders to deliver production-grade data products and maintain pipeline reliability.

Lead the design and delivery of enterprise-scale data platforms and pipelines, using modern data platforms, processing frameworks, streaming technologies, and cloud services. Build, lead, and mentor a data engineering team; support client relationships, proposals, migrations, governance, security, and compliance. Contribute technical thought leadership through blogs and community engagement while delivering solutions from proof of concept through commercialization.

Manages and expands public-sector client accounts by building relationships, ensuring satisfaction, reducing churn, and driving revenue and margin growth. Develops account strategies, identifies opportunities, coordinates global stakeholders and partners, supports clients throughout the buying lifecycle, and manages pipelines, forecasts, and quotas in Salesforce.com. Serves as the primary client contact, resolves issues, and translates business needs into tailored technology solutions.