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Salary
$66k – $85k per year (gross)
Location
In office (Singapore)
Seniority
Staff · 7+ years exp
Employment
Full-Time

First seen by Alion on Oct 5, 2026.

Overview
Company
Impact
Profile match
OPUS IT Services Pte Ltd is a leading IT services and solutions company in Singapore. OPUS focuses on 3 core business services.
  • Location: Ang Mo Kio
  • Working Hours: Mon – Fri, 8:30am – 6:00pm
  • Salary: basic + $50 allowance
  • Contract: 1-year (renewable)
  • Eligibility: Native born Singaporeans only (due to Cat 1 G50 clearance)

Role Overview

We are looking for a Data Analytics Solution Architect to design and lead enterprise-scale data, analytics, infrastructure and cloud solutions. The role bridges business, data, application, infrastructure and cloud teams, ensuring secure, scalable and high-performance solutions aligned with enterprise and government requirements.

Key Responsibilities

  • Design end-to-end data analytics architectures, including data warehouses, data lakes, lakehouses, data pipelines and analytics/reporting platforms.
  • Translate business and technical requirements into scalable, secure and cost-effective solution architectures.
  • Lead data integration, ETL/ELT, data modelling, data governance, metadata, data quality and data lineage strategies.
  • Design architectures across public/private cloud, hybrid and on-premises environments, covering compute, storage, networking, security and platform services.
  • Provide enterprise architecture leadership for Government Commercial Cloud (GCC) and MINDEF Commercial Cloud (MCC) environments.
  • Establish architectural standards for data platforms, cloud services, infrastructure, security and technology roadmaps.
  • Guide the use of cloud-native services, Docker, Kubernetes, APIs, CI/CD and Infrastructure-as-Code (IaC).
  • Drive platform performance, scalability, reliability and cost optimization across data and infrastructure layers.
  • Ensure compliance with government security, privacy, governance and regulatory requirements, including secure data access, encryption, key/secrets management and auditability.
  • Lead architectural reviews, technical assurance and governance activities, while mentoring engineers and communicating solutions to senior stakeholders.

Key Technical Requirements

1. Strong experience in data architecture, data engineering and enterprise architecture, including:

  • Data warehousing, data lakes/lakehouses and analytics platforms
  • ETL/ELT and data integration
  • Conceptual, logical and physical data modelling
  • Dimensional modelling / Data Vault / domain-oriented modelling
  • Data governance, metadata, lineage and data quality

2. Strong understanding of enterprise infrastructure architecture covering compute, storage, networking and security.

3. Hands-on experience with AWS, Azure or GCP, particularly in GCC and MCC environments.

4. Strong programming/scripting skills in SQL and Python.

5. Experience with Docker, Kubernetes, cloud-native data services, CI/CD and IaC such as Terraform or CloudFormation.

6. Understanding of data security architecture, API integration, event-driven/streaming architectures and platform observability.

7. Knowledge of machine learning/advanced analytics pipelines and MLOps is an advantage.

Requirements

  • Bachelor’s/Master’s degree in Computer Science, Data Science, Engineering or a related field.
  • Minimum 7 years' experience in data architecture, enterprise architecture, data engineering or related roles.
  • Proven experience delivering large-scale enterprise data analytics and cloud solutions.
  • Must have prior MINDEF project experience.
  • Must have been previously security-cleared for CAT 1 / G50.
  • Strong understanding of MINDEF security, governance and compliance requirements.

Skills

Security Architecture, Data Modelling, Kubernetes, GCC, AWS, ETL, Data Integration, Infrastructure as Code (IaC), Data Governance, Data Engineering, Data Lineage, SQL, Data Lake, Solution Architecture, Enterprise Architecture, Python, Data Architecture, Docker, Data Infrastructure, Data Warehousing

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