In 2026, businesses are doubling down on digital transformation to stay competitive and agile in an ever-evolving market. While areas like e-commerce and digital marketing remain vital, emerging technologies such as artificial intelligence, cybersecurity, and sustainable tech are leading the way.
Demand remains particularly strong for professionals with specialized capabilities. Singapore’s Ministry of Manpower found that software developers, data scientists and systems programmers were among the professional roles that remained difficult to fill in 2025. A lack of specialized skills and relevant experience were the main challenges reported by employers.
Singapore’s technology workforce also grew from 208,300 professionals in 2023 to 214,000 in 2024, led partly by growth in artificial intelligence, data and cybersecurity roles, according to IMDA’s Singapore Digital Economy Report.
Here are seven technology roles companies should consider when building their teams in 2026.
1. Cybersecurity Specialist
A cybersecurity specialist protects company systems, networks and information from unauthorized access, disruption and misuse.
The role has become increasingly important as companies adopt cloud services, support distributed teams and manage larger volumes of sensitive data. IMDA reported that cybersecurity was among the areas contributing to growth in Singapore’s technology workforce.
Responsibilities can include:
- Monitoring security threats
- Conducting vulnerability assessments
- Managing identity and access controls
- Responding to security incidents
- Reviewing cloud and application security
- Supporting data protection and regulatory compliance
Employers may look for experience in security operations, penetration testing, risk management, incident response or cloud security.
The exact profile you need depends on your risks. A financial services company handling regulated data may require different expertise from a retail company securing an ecommerce platform.
2. Cloud Engineer
A cloud engineer designs, deploys and maintains infrastructure on platforms such as Amazon Web Services, Microsoft Azure or Google Cloud.
Cloud engineers help businesses improve scalability, system availability and deployment speed. They may also help control infrastructure costs and support disaster recovery.
Common responsibilities include:
- Configuring cloud infrastructure
- Automating deployments
- Monitoring system performance
- Managing backups and recovery
- Implementing access controls
- Optimizing cloud costs
Useful skills include Linux, networking, containers, infrastructure as code and cloud security.
Businesses should look beyond platform certifications when evaluating candidates. Strong cloud engineers also understand architecture, reliability, security and the commercial impact of infrastructure decisions.
3. DevOps and Site Reliability Engineer
DevOps and site reliability engineers improve the connection between software development and technology operations.
Their goal is to help teams release software more reliably while reducing system failures and manual work.
Responsibilities may include:
- Creating continuous integration and delivery processes
- Automating infrastructure management
- Monitoring application performance
- Improving system reliability
- Managing incident response
- Supporting development teams
Relevant skills include cloud platforms, containers, automation, monitoring tools, scripting and infrastructure as code.
This position becomes particularly valuable when a company is releasing software frequently, managing complex infrastructure or experiencing recurring reliability issues.
4. Artificial Intelligence Engineer
An artificial intelligence engineer develops systems that can analyze information, generate content, automate decisions or make predictions.
Their responsibilities may include:
- Building and training machine learning models
- Integrating artificial intelligence into existing products
- Creating generative artificial intelligence applications
- Evaluating model accuracy and reliability
- Monitoring performance after deployment
- Working with security, legal and product teams on responsible use
Artificial intelligence engineers commonly need experience with Python, machine learning frameworks, application programming interfaces, cloud platforms and data pipelines. Increasingly, employers also look for knowledge of model governance, privacy and artificial intelligence security.
This role is valuable when a company has a defined business problem that artificial intelligence can solve. Hiring an engineer without a clear use case can lead to costly experiments that never reach production.
5. Software Engineer
Software engineers design, build, test and maintain digital products and internal systems.
They can specialize in frontend development, backend development, mobile applications, platform engineering or full stack development. The right specialization depends on the product and existing technology environment.
Singapore’s 2025 Job Vacancy Survey found that software, web, multimedia and games developers represented the largest share of professional vacancies that remained open for at least six months among the roles highlighted in the report.
Important capabilities may include:
- Programming languages relevant to your technology stack
- System design
- Software testing
- Version control
- Database management
- Security and performance optimization
Employers should assess how candidates approach problems rather than relying entirely on coding tests. Architecture decisions, communication and collaboration often determine whether an engineer can succeed in a growing team.
6. Data Engineer
A data engineer creates the infrastructure that collects, stores and prepares company data.
While data scientists analyze information, data engineers make sure that the information is accurate, accessible and ready for analysis. Without this foundation, dashboards, predictive models and artificial intelligence tools may produce unreliable results.
Typical responsibilities include:
- Building data pipelines
- Connecting data from different systems
- Managing databases and data warehouses
- Improving data quality
- Monitoring data infrastructure
- Establishing access and governance controls
Relevant skills include SQL, Python, cloud data services, data modelling and extract, transform and load processes.
Companies usually need a data engineer when information is spread across multiple platforms or when manual reporting is slowing down decision making.
7. Data scientist
A data scientist uses statistical analysis, programming and business knowledge to extract useful insights from data.
Depending on the organization, a data scientist may develop forecasting models, identify customer patterns, measure operational performance or support artificial intelligence initiatives.
Important skills include:
- Statistical analysis
- Python or R
- Machine learning
- Data visualization
- Experimental design
- Business communication
The ability to explain findings is just as important as technical knowledge. A complex model has limited value if business leaders cannot understand or act on its results.
Before hiring a data scientist, confirm that your business has reliable data, a meaningful question to answer and a team capable of implementing the findings.
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Glints TalentHub is our complete talent solution that covers all aspects of talent acquisition, management, and development. Our team can help you access the extensive talent pool across Southeast Asia and build great teams quickly and affordably. Simply schedule a call with us and our dedicated experts will be thrilled to guide you in your journey towards building a borderless team.
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