Total 1,336 data entry job vacancies in Information Technology category in Singapore

Job Summary

We are looking for Data Engineer who will be playing a key role in creating, building and maintaining our client’s data infrastructure.

Mandatory Skill-set

• Degree in Computer Science or related discipline;
• Minimum 2-3 years of experience in Data Analytics;
• Must have Good hands-on experience in both SQL, Python, Pyspark;
• At least 2 years of experience in Data Management and data governance;
• Able to collect, parse, manage, analyse and visualise large sets of data to turn information into insights accessible through multiple platforms and works more with structured data;
• Basic project management knowledge, Team presentation skill & Business analytical skill;
• Excellent interpersonal, and communication skills with the ability to coordinate / work with multiple teams;
• Motivated, driven, logical, and possess excellent time management skills;
• Has strong analytical and problem-solving skills with attention to detail.

Desired Skill-set

• Experience in financial industry;
• Experience in Data science;
• Familiarity with Agile development methodologies.

Responsibilities

• Create, build, and maintain the data infrastructure required for Extraction, Transformation and Loading of data from a wide variety of sources.
• Ensure high-quality data is delivered to support Compliance / FCC model development.
• Support the implementation of Data Analytics / Automation for Compliance teams and Business stakeholders.
• Liaise with relevant stakeholders (e.g., Data Management Office) to identify and assess suitability of data for Compliance / FCC Data models.
• Liaise with Business stakeholders / analysts and ensure assembled data assets used for model development meet business requirements.
• Build & maintain data dictionaries / data requirements of implemented data models and coordinate with Data Scientist(s) in developing model narratives.  
• Monitor the execution & performance of data models and liaise with IT for any technical / system outages.
• Keep track of datasets / tables in the working schema and support purging of unused tables periodically.
• Perform data analysis required to troubleshoot data related issues and support the resolution of raised data issues.

Should you be interested in this career opportunity, please send in your updated resume to apply@sciente.com at the earliest.

When you apply, you voluntarily consent to the disclosure, collection and use of your personal data for employment/recruitment and related purposes in accordance with the SCIENTE Group Privacy Policy, a copy of which is published at SCIENTE’s website (https://www.sciente.com/privacy-policy).

Confidentiality is assured, and only shortlisted candidates will be notified for interviews.

EA License No. 07C5639

JOB OVERVIEW:

The Data Scientist (AI Engineer) serves as the critical technical bridge between high-level business strategy and scalable AI production. This role is dedicated to accelerating demand creation by translating complex client challenges into feasible, high-impact AI/ML prototypes and Proof of Concepts (PoCs).

By balancing technical rigor with strategic communication, the incumbent assesses data maturity, designs agile solutions using both traditional and Generative AI, and ensures that all innovations are architecturally sound and ready for production handover. Ultimately, this role drives organizational growth by de-risking AI investments and demonstrating the tangible technical value of emerging technologies.

Core Focus Areas

• Technical Translation: Converting business "pain points" into rigorous technical requirements and feasible AI architectures.

• Rapid Innovation: Designing and iterating on interactive prototypes that showcase the "art of the possible" while adhering to vetted Technology Readiness Level (TRL) standards.

• Strategic Advisory: Evaluating client data infrastructure and providing technical roadmaps to bridge the gap between current capabilities and AI readiness.

• Seamless Integration: Partnering with Production and Enablement leads to ensure prototypes are scalable, documented, and transitioned without technical debt.

• Technology Scouting: Researching and applying cutting-edge tools from the internal Tech Radar to solve real-world client problems.

DUTIES AND RESPONSIBILITIES:

Technical Requirements Elicitation & Analysis:

• Collaborate with AI/ML Business Analysts and clients to deeply understand their existing technical landscape, data infrastructure, and specific operational challenges.

• Translate high-level business needs into technical requirements for AI/ML solutions, identifying necessary data inputs, system integrations, and potential technical constraints.

• Assess the technical feasibility of proposed AI/ML use cases, ensuring alignment with the organization’s vetted tech stack and TRL standards, given the client's current data capabilities and technology stack.

AI/ML Solution Conceptualization & Prototyping:

• Design and develop rapid, interactive prototypes or proof-of-concepts (PoCs) using appropriate AI/ML techniques (both traditional ML and cutting-edge AI like Generative AI).

• Demonstrate the functionality and potential business impact of AI/ML solutions through compelling technical demonstrations and visualizations.

• Iterate rapidly on prototypes based on client feedback, showcasing agility and responsiveness.

Technical Communication & Value Articulation:

• Simplify complex AI/ML concepts, algorithms, and technical architectures into understandable terms for non-technical business stakeholders.

• Clearly articulate the technical value proposition of proposed solutions, explaining how the AI/ML will work and why it is the right approach for the client's problem.

• Present technical findings, prototype results, and architectural considerations to clients and internal teams.

Organizational Data Capability Assessment (Technical):

• Conduct technical assessments of client data maturity, including data quality, accessibility, governance, and existing infrastructure.

• Advise clients on technical prerequisites and potential roadmaps for enhancing their data capabilities to support AI/ML initiatives.

Emerging Technology Research & Application:

• Actively research and evaluate new AI/ML technologies, tools, and frameworks (e.g., specific algorithms, cloud AI services, MLOps tools) relevant to demand creation and prototyping.

• Support the AI Solution Enablement Lead by exploring specific application-layer use cases for technologies already on the internal Tech Radar. Provide field feedback on the performance of vetted tools in client prototypes

• Identify how emerging technical capabilities can be applied to solve client problems and create new service offerings.

• Contribute technical insights to market exploration and strategic planning.

Cross-Functional Collaboration & Handover:

• Work in close partnership with the Data Science / AI Production Team from the earliest stages to ensure that prototyped solutions are designed with production scalability and maintainability in mind.

• Collaborate primarily with the AI Solution Enablement Lead to transition validated TRL 3 prototypes into the hardening phase. Ensure all initial technical elicitation is documented according to Enablement Quality Gate standards to prevent technical debt during handover

• Facilitate smooth technical handovers of validated concepts, prototypes, and initial architectural considerations to the production team, minimizing rework and ensuring continuity.

• Provide technical context and support during the transition phase to ensure the production team has a comprehensive understanding of the solution's intent and design.

• Problem Solving & Innovation:

• Apply strong analytical and problem-solving skills to technical challenges encountered during the demand creation and prototyping phases.

• Propose innovative technical approaches to address client needs and differentiate our solutions.

QUALIFICATIONS AND EXPERIENCES:

• Minimum Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field. Master's or Ph.D. preferred.

• At least 5+ years of progressive experience in AI/ML solution design, data science, or AI engineering, with a significant portion in a client-facing or consulting capacity.

• Proven experience in designing, developing, and deploying functional AI/ML prototypes that demonstrate business value and are engineered for production.

• Expert proficiency in Python and extensive experience with core AI/ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).

• Demonstrated hands-on experience with Generative AI and Large Language Models (LLMs), including frameworks like LangChain, for designing and prototyping novel applications.

• Strong practical experience with Google Cloud Platform (GCP) services relevant to AI/ML (e.g., Vertex AI, BigQuery, Cloud Storage, Dataflow, Cloud Functions).

• Proficiency with Docker for containerization and a foundational understanding of container orchestration (e.g., Kubernetes concepts for GKE).

• Solid understanding of MLOps principles for reproducibility, experiment tracking (e.g., MLflow, Vertex AI ML Metadata/Model Registry), and model versioning.

• High-level expertise in data governance frameworks, data quality assessment (including tools/libraries like Great Expectations), metadata management (e.g., Google Cloud Data Catalog), and data architecture principles.

• Strong expertise in distributed computing and parallel processing techniques is vital for handling large-scale AI workloads. 

• A proven track record of innovations and executions in deep learning, demonstrated through shipping products or first-author publications at leading AI conferences, is a strong differentiator.

• Experience in the container shipping industry or related logistics/supply chain domains is highly advantageous.

• Excellent presentation, documentation, and stakeholder management skills.

• Experience Requirements:
• Must have QA/Testing Experience.
• Data Testing Experience specifically in Big Data, Hadoop, or Cloud Data Warehouse environments.
• Databricks Experience of testing pipelines within a Databricks environment.
• Automation Focus: Proven track record of moving from manual SQL checks to automated Python-based testing frameworks.
• Mandatory: Databricks Certified Data Engineer Associate (at minimum).
• Preferred: ISTQB Foundation or Advanced Level (Test Automation Engineer).
• Core Technical Skills:
• Data Validation & Frameworks
• Great Expectations / Pandera: Proficiency in using Python-based libraries to define data "contracts" and automated validation suites.
• DLT Expectations: Deep understanding of Delta Live Tables (DLT) expectations (Fail, Drop, Quarantining bad records).
• Advanced SQL: Expert-level SQL for complex data reconciliation, identifying duplicates, and null-value analysis across billions of records.
• Python for QA (PySpark):
• Pytest-Spark: Experience using pytest to write unit tests for PySpark transformations and logic.
• Notebook Testing: Ability to write automated test notebooks that validate Medallion Architecture transitions (Bronze to Silver, Silver to Gold).
• Data Reconciliation: Building Python scripts to perform "source-to-target" counts and checksums across distributed file systems.
• Performance & Integration Testing:
• Scalability Testing: Ability to validate that data pipelines meet performance SLAs when data volume spikes.
• End-to-End Orchestration Testing: Testing the reliability of Databricks Workflows and handling of job failures/retries.
• Schema Evolution: Testing how pipelines handle upstream schema changes without breaking downstream Gold tables.
• Governance & Security Testing:
• Unity Catalog Validation: Testing Row-Level Security (RLS) and Column-Level Masking to ensure unauthorized users cannot see sensitive data.
• Data Lineage: Validating that data lineage in Unity Catalog correctly reflects the movement of data across the Lakehouse.

CLC is an investment management firm with offices in Singapore and Hong Kong. Applicants are invited to apply for a data analyst position based in Singapore. The position provides the opportunity for global exposure within a supportive and performance-driven team environment.

Key responsibilities:

• Work with data sets across various data sources and conduct data analysis to identify insights
• Build and maintain models, databases and data analytics tools
• Conduct analysis and research related to companies, industries, economies and financial markets
• Ensure quality of data and enhancements
• Produce internal reports and presentations

Key requirements:

• Strong academic background with degree in finance, economics, mathematics, statistics, computer science, engineering or other quantitative discipline
• Experience in data analytics, quantitative analysis or statistical computing well-regarded
• Relevant industry qualification well-regarded

• Excellent analytical, modelling and numerical skills
• Driven and passionate about data analytics and research
• Reliable, accurate and detail-oriented with strong work ethic
• High professional standards and integrity
• Good written and verbal communication skills

Competitive remuneration commensurate with experience. Qualified applicants should submit their resume and a cover letter.

Only shortlisted candidates will be notified. All data will be used for recruitment purposes only.

This is a contract role ending on 14 Apr 2027.

Our client, a reputable multinational fintech company, is seeking an experienced Data Engineer to join their growing data team in Singapore. This role offers the opportunity to work on scalable data platforms and drive data-driven decision-making across the organization.

Responsibilities

• Understand and analyze complex datasets to generate actionable insights
• Collaborate with BSA, domain teams, and stakeholders to gather requirements and translate them into scalable technical solutions, including ETL pipelines and data visualizations
• Design, implement, and maintain CI/CD pipelines for data engineering workflows
• Develop and maintain BI dashboards based on business requirements
• Ensure data security, governance, and compliance with organizational standards
• Troubleshoot and resolve data gaps, anomalies, and pipeline issues to ensure data integrity and reliability

Requirements

• 5+ years of experience in data engineering and SQL
• Experience with big data technologies (e.g., Hadoop, Spark, Hive)
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• Proficiency in programming languages such as Python or Java
• Experience with data visualization tools (e.g., Tableau, Power BI, Looker)
• Solid understanding of data modeling, data warehousing, and ETL design patterns
• Familiarity with Agile methodologies and product development lifecycle
• Experience with cloud platforms (e.g., GCP, BigQuery)
• Exposure to fintech or payments domain will be good to have

· A bachelor's or technical degree in Computer Science, IS, or IT is preferred

· At least 8 years of relevant working experience in Java/J2EE development

· Hands-on experience in big data technology development is preferred

· Good understanding & working knowledge with Agile/Scrum

· Hands-on experience of end-to-end design and implementation of complex IT systems

· Solution design using proven patterns, performance tuning.

· DevOps/release management/production support skills strongly preferred.

· Java/J2EE, Java 1.8 and above is preferred to implement parallelism and concurrency.

·        Must have knowledge on Spark/Flink/Storm to analyze and transform large amounts of data.

·        Must have knowledge of HBase and Elasticsearch to perform CRUD operations on big data.

·        Must have good understanding on IBM MQ, Kafka for message processing.

·        Good knowledge on Cloud Platform (AWS, GCP,etc)

·        Should have knowledge of Maven, gradle, Jenkins, UML, GitHub Copilot tools to manage and build project.

·        Should have knowledge on GIT, GIT Lab, Junit, Cucumber, Jira, Sonar to manage/test source code effectively.

·        Should have good knowledge on Spring, Spring REST, Web services, gRPC, Micro services,framework to implement robust API’s.

·        Must have knowledge on Kibana, spark UI, ELK to debug and identify the root cause of the issues.

About our group:

Seagate Research Group (SRG) conducts research to transform Seagate’s considerable technical expertise, innovation, and manufacturing excellence to develop innovative technologies for the rapidly growing datasphere. 

SRG - Data Trust (DT) Vector focuses on increasing security & privacy capabilities to anchor Seagate as the industry leader of trustworthy storage solutions. 

Currently, the DT team is seeking a candidate to research exciting emerging cryptographical algorithms, protocol and/or security technologies. This position offers a variety of opportunities to prototype, experiment, and benchmark innovative ideas and concepts in projects.  

About the role - you will:

As a Data Trust Security Researcher, you will collaborate with a team of researchers and engineers to explore and evaluate next‑generation security technologies. In this role, you will:

Prototype and evaluate cryptographic algorithms, including Post‑Quantum Cryptography (PQC) and conventional cryptography

Assess and validate security protocols for embedded and resource‑constrained environments

Explore memory‑safe programming languages for secure embedded implementations

Research the evolving threat landscape and identify applicable mitigation techniques

Work with Seagate Research teams on topics such as cryptography, product security, and data security

Conduct performance benchmarking, robustness analysis, and experimental validation of security techniques

Contribute to proof‑of‑concepts and experimental platforms involving cryptography, hardware trust, or secure embedded systems

About you:

We’re Looking for Someone Who Is:

Knowledgeable in cryptography, security standards, and protocols

Knowledgeable with memory‑safe languages

A collaborative team player with a proactive, learning‑oriented mindset

Able to clearly articulate technical concepts to both technical and cross‑functional audiences

Your experience includes:

PHD in Cryptology, mathematics or any relevant fields.

Implementing or designing cryptographic algorithms

Applying or researching Post‑Quantum Cryptography (PQC)

Software development

Working with embedded systems programming and debugging security-related projects an advantage

Experience with biometric security systems is an advantage

Development experience in Linux environments

FPGA development or hardware‑accelerated implementations for cryptography or security projects

Role Accountabilities (What is this role responsible for delivering?)

• Define and execute a comprehensive data and AI strategy aligned with business objectives.
• Champion a data-driven culture across the organization.
• Lead the development of enterprise data platforms, ensuring scalability, security, and performance.
• Establish and enforce data governance frameworks, including data quality, lineage, stewardship, and metadata management.
• Ensure compliance with global and regional data privacy regulations (e.g., GDPR, PDPA).
• Oversee data risk management and audit readiness.
• Lead data teams, including data science to identify AI/ML opportunities, partnership with business partners.
• Ensure data readiness and infrastructure to support model development and deployment.
• Implement AI governance practices to ensure ethical, explainable, and responsible AI use.
• Lead the design and implementation of integration architectures. (e.g., APIs, event-driven systems, data pipelines)
• Drive modernization of legacy systems to support real-time and scalable integrations.
• Oversee the development and maintenance of data lakes, warehouses, and real-time data platforms.
• Ensure alignment with enterprise architecture and cloud strategies (e.g., Azure, AWS, GCP).
• Promote reusable data assets and self-service analytics capabilities.
• Collaborate with C-level executives, business leaders, and IT to align data initiatives with strategic goals.
• Drive change management efforts to embed data and AI into business processes.
• Communicate data value and insights to non-technical stakeholders.
• Build and lead high-performing teams across data engineering, integration, and governance.
• Foster a culture of innovation, continuous learning, and cross-functional collaboration.
• Develop talent pipelines and upskill teams in AI, data engineering, and integration technologies.

People Management

• Lead employees to meet the organisation's expectations for productivity, quality, and goal accomplishment.
• Provide oversight and direction to the employees in the team in accordance with the organisation's policies and procedures.
• Plan staffing levels and work with Human Resources to recruit, interview, select, hire, and employ the right people within budgetary guidelines.
• Coach, mentor and develop staff, including overseeing new employee onboarding and providing career development planning and opportunities.
• Consciously create an engaging workplace culture that emphasizes the identified mission, vision, guiding principles, and values of the organisation.
• Lead employees using a performance management and development process including goal setting, feedback, and performance development planning.
• Empower employees to take responsibility for their jobs and goals. Delegate responsibility, drive accountability and provide regular feedback for continuous improvement.
• Manage and maintain effective employee work schedules including assignments, job rotation, training, vacations and paid time off, cover for absenteeism, and overtime scheduling.
• Maintain two-way transparent communication. Appropriately communicate organization information through department meetings, one-on-one meetings, and appropriate email, IM, and regular interpersonal communication.
• Deliver key people related activities in conjunction with the People Division that will lead to the enhanced engagement and retention of team members.
• Champions team collaboration by fostering an inclusive environment that encourages open communication and the sharing of ideas across all levels.
• Drives cross-functional initiatives by building partnerships and influencing stakeholders to achieve common goals without formal authority.
• Leads by example, demonstrating accountability, resilience, and a solution-oriented mindset to inspire others.
• Promotes the adoption of best practices by identifying process improvements and advocating for positive change within the team.
• Influences decision-making by presenting data-driven insights and articulating the team’s perspectives to management and other departments.
• Promotes a culture of recognition by celebrating team achievements and encouraging a sense of shared success.
• Supports the onboarding and integration of new team members, helping to build a cohesive and high-performing unit.

Crisis Management

• Make every effort to be aware of and understand your role in the event of a crisis.
• Make every effort to attend any relevant training, simulations and or meetings relating to Crisis Management.
• Perform your role as best as possible in a calm and collaborative manner in line with the relevant policy and procedures in the event of a Crisis.
• Provide input and feedback towards the ongoing improvement of the Crisis Management framework and procedure.

Key Qualifications & Skills (What knowledge will ensure success in the role?)

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Artificial Intelligence or related field.
• Master’s or PhD preferred for strategic leadership roles.
• 10+ years in data-related roles. (engineering, analytics, science, governance)
• 7+ years in leadership or strategic roles.
• 5+ years governance related experience.

Certifications (optional but valuable):

• Data Science & AI Foundations
  - Data Science Specialization – Johns Hopkins (Coursera)
  -AI for Everyone – Andrew Ng (Coursera)

• Professional Certificate in Machine Learning and AI – edX (MIT or Columbia)
  - Executive & Strategic AI
  - Oxford Executive Program in AI
  - MIT Sloan – Artificial Intelligence: Implications for Business Strategy
  - INSEAD – AI for Business
  - AWS/GCP/Azure Data Engineering or Architecture certifications
  - TOGAF (for enterprise architecture)

• Responsible AI & Governance
  - Responsible AI Certification – World Economic Forum / IEEE
  - AI Ethics and Governance – Singapore Management University (SMU
  - Certified Data Management Professional (CDMP)
  - PMP or Agile certifications (for project leadership)

• Proven track record of building and scaling data teams and platforms.
• Must be able to interact effectively across multiple levels of the organization.
• Strong problem-solving abilities, with a data-driven and analytical approach to process improvement.
• Excellent communication and interpersonal skills, facilitating collaboration between technical and non-technical stakeholders.
• Knowledge and experience in executive reporting.

Technical Skills

Data Architecture & Engineering:

• Designing scalable data platforms. (data lakes, warehouses, marts)
• ETL/ELT pipelines using tools like Apache Airflow, dbt, Informatica.

Cloud Platforms:

• Deep expertise in AWS, Azure, or Google Cloud.
• Services like Redshift, BigQuery, Snowflake, Databricks.

Programming & Tools:

• Python, SQL, Spark, Scala.
• Familiarity with R, Java, or Julia is a plus.

Data Governance & Quality:

• Master data management (MDM), metadata management.
• Data lineage, cataloging. (e.g., Collibra, Alation)

Analytics & BI:

• Tools: Power BI, Tableau, Looker.
• Experience with KPI frameworks and dashboarding.

Machine Learning & AI (if applicable):

• Model lifecycle management, MLOps.
• Experience with ML platforms. (e.g., SageMaker, Vertex AI)

Security & Compliance:

• GDPR, CCPA, HIPAA. (depending on industry)
• Data encryption, access controls, audit trails.

Professional Skills

Strategic Thinking:

• Aligning data strategy with business goals.
• Driving data monetization and innovation.

Leadership & Team Building:

• Hiring, mentoring, and managing cross-functional data teams.
• Fostering a data-driven culture.

Stakeholder Engagement:

• Communicating with C-suite, product, engineering, and business units.
• Translating data insights into business value.

Project & Change Management:

• Leading data transformation initiatives.
• Managing budgets, timelines, and vendor relationships.

Communication & Storytelling:

• Presenting complex data insights in a compelling way.
• Influencing decisions through data narratives.

Adaptability & Continuous Learning:

• Staying current with data trends, tools, and regulations.
• Encouraging experimentation and innovation.

By submitting your CV, you are deemed to have agreed and consent to the collection, use and/or disclosure of your personal data to the Company for the purposes of recruitment and employment only in accordance with the Company's Recruitment Data Privacy Policy on our website.

Swire Shipping is committed to ensuring equal equitable access and participation for persons who experience disability. SSL is committed to treating persons with disability in a way that allows them to maintain their dignity and independence. Reasonable adjustments can be made upon request based on understanding of those accommodations.

Across Swire Shipping, we seek to create a supportive and inclusive environment that embraces individuality and recognise the benefits that these differences make. We do this by ensuring that all individuals are treated with respect and understanding and actively promote Equal Employment Opportunity (EEO) and do not tolerate discrimination, harassment, bullying, retaliation or intimidation of any kind. We are committed to driving the strategy, policies and accountability to build and sustain a diverse global workforce, through equitable processes and systems where everyone can contribute their very best.

About the Role

We are looking for a skilled and proactive Database Engineer to design, implement, and maintain robust, scalable, and secure database systems. You will play a key role in managing both cloud and on-premise data platforms, ensuring high performance, reliability, and data integrity across the organisation.

Key Responsibilities

Database Implementation & Operations

• Design, deploy, and manage databases in alignment with architectural standards and technical specifications
• Configure and maintain data storage solutions across relational, non-relational, and hybrid environments
• Manage cloud-based database infrastructures with a focus on cost efficiency, scalability, and performance

Performance & Optimisation

• Identify and implement database tuning strategies, including indexing and query optimisation
• Monitor and troubleshoot performance issues across relational and NoSQL database systems

Data Lifecycle & Reliability

• Define and enforce data lifecycle policies, including retention, archival, and deletion strategies
• Develop and maintain robust backup and recovery processes to ensure data availability and integrity

Security & Compliance

• Ensure database security, including access controls and adherence to organisational and regulatory requirements
• Collaborate with cross-functional teams to support data governance, compliance, and audit initiatives

Automation & Efficiency

• Automate routine database administration tasks to improve operational efficiency
• Leverage scripting and automation tools to streamline deployment, monitoring, and maintenance processes

Qualifications & Skills

• Bachelor’s Degree in Computer Science, Information Systems, or a related discipline
• Minimum 5 years of experience in database administration across multiple database architectures
• Strong experience with relational databases such as Microsoft SQL Server and PostgreSQL
• Hands-on experience with NoSQL databases (e.g., Cassandra, MongoDB) is an advantage
• Familiarity with object storage solutions such as AWS S3 or Nutanix Object Store
• Knowledge of graph databases (e.g., Neo4j) is a plus
• Strong expertise in performance tuning, indexing, and query optimisation
• Experience in data lifecycle management, including retention and archival strategies
• Proficient in scripting and automation using Python, Bash, or PowerShell
• Strong analytical thinking, problem-solving abilities, and stakeholder collaboration skills

Work Location: One North

(EA Reg No: 20C0312)

Please email a copy of your detailed resume to kayla@talentsis.com.sg for immediate processing.

Only shortlisted candidates will be notified.

Qualifications & Requirements

Bachelor’s degree in Electrical Engineering, Mechanical Engineering, or a related field.

Experience

• 5–10+ years of experience in data center engineering, capacity planning, or infrastructure management.
• Experience with hyperscale or colocation data centers preferred.
• Proven experience in capacity modeling and forecasting.

Strong knowledge of:

Data center power and cooling systems (UPS, HVAC, CRAC/CRAH)

Rack capacity planning and space optimization

Network and compute infrastructure basics

Familiarity with DCIM tools and monitoring systems.

Understanding of redundancy standards (e.g., N, N+1, 2N)