Data Engineer

XCELLINK PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

XCELLINK PTE. LTD. is seeking a Data Engineering & Analytics Engineer to design, build, and support enterprise-grade data platforms and pipelines across on-premises and cloud environments.

You will work with software and platform engineers to deliver scalable and secure data solutions, enabling reliable analytics and AI/ML workloads. The role focuses on end-to-end data lifecycle, governance, and trusted datasets for reporting, analytics, and applications, with emphasis on data quality, security,

Qualifications

• Degree in CS/IT/Engineering or related field.
• 3–5 years in Data/Analytics/Cloud data engineering or related.
• Hands-on experience designing and supporting production-grade data pipelines.
• Experience with AWS and/or Azure cloud services.
• Familiarity with CI/CD, IaC, and data governance practices.

Responsibilities

• Design, build, and maintain production-grade ETL/ELT data pipelines.
• Integrate data from enterprise applications, APIs, databases, SaaS, cloud services, files, and streaming sources.
• Develop batch, CDC, event-driven, and streaming ingestion solutions.

Skills

Python

SQL

ETL/ELT

CDC

Streaming

Data modeling

CI/CD

Terraform

Cloud platforms

Education

Degree in CS/IT/Engineering

Tools

AWS

Azure

Terraform/OpenTofu

Git

GitLab CI/CD

Job description

We are seeking a Data Engineering & Analytics Engineer to design, build, and support enterprise-grade data platforms and pipelines. This role is responsible for the end-to-end data lifecycle, including ingestion, transformation, modelling, quality management, governance, and delivery of trusted datasets for applications, reporting, analytics, and AI/ML workloads.

The successful candidate will work closely with software engineers, platform engineers, and business stakeholders to deliver scalable, secure, and reliable data solutions across on-premises and cloud environments.

Key Responsibilities

Data Engineering & Integration

• Design, build, and maintain production-grade ETL/ELT data pipelines.
• Integrate data from enterprise applications, APIs, databases, SaaS platforms, cloud services, files, and streaming sources.
• Develop batch, incremental, change data capture (CDC), event-driven, and streaming data ingestion solutions.
• Implement data transformation processes to cleanse, enrich, standardise, and consolidate data into trusted datasets.
• Design secure and resilient data integration solutions across on-premises, GCC, AWS, Azure, and other approved environments.
• Automate deployment, testing, monitoring, and operational processes for data platforms.

Data Architecture & Analytics

• Develop and maintain data lakes, analytical datasets, and cloud-native data platforms.
• Design logical and physical data models to support operational reporting, analytics, and machine learning initiatives.
• Create reusable data products and datasets that support business intelligence, operational visibility, and decision-making.
• Collaborate with stakeholders to translate business requirements into robust data solutions.

Data Quality, Governance & Security

• Implement data validation, reconciliation, monitoring, and quality controls throughout the data lifecycle.
• Monitor pipeline health, data freshness, completeness, and reliability.
• Maintain data lineage, metadata, auditability, and traceability.
• Ensure compliance with security policies, access controls, data classification, retention, and governance requirements.

Operations & Continuous Improvement

• Support production data platforms and pipelines.
• Investigate incidents, perform root cause analysis, and implement preventive measures.
• Monitor platform performance, reliability, scalability, and costs.
• Maintain technical documentation, runbooks, and operational procedures.
• Drive continuous improvements in automation, reliability, and operational efficiency.

Requirements

Experience

• Degree in Computer Science, Information Technology, Engineering, Data Analytics, or a related discipline.
• Minimum 3 to 5 years of experience in Data Engineering, Analytics Engineering, Cloud Data Engineering, Software Engineering, or related fields.
• At least 2 years of hands‑on experience designing, building, and supporting production‑grade data pipelines.
• Experience with data extraction, ingestion, ETL/ELT, data transformation, modelling, and quality management.
• Experience integrating data from APIs, databases, enterprise systems, files, and streaming platforms.
• Experience with AWS and/or Microsoft Azure cloud services.
• Familiarity with hybrid cloud and on‑premises integration architectures.
• Experience applying software engineering best practices including version control, CI/CD, automated testing, monitoring, and Infrastructure as Code.

Technical Skills

• Python and SQL development.
• Data engineering concepts including ETL/ELT, CDC, streaming, batch, and event‑driven architectures.
• Data modelling techniques including relational, dimensional, and analytical modelling.
• Data quality management, validation, reconciliation, and monitoring.
• Infrastructure as Code (Terraform/OpenTofu).
• CI/CD tools such as GitLab CI/CD and SHIP-HATS.
• Analytics, reporting, and data preparation for BI and AI/ML use cases.
• AWS and/or Azure native data, storage, analytics, and integration services.

Preferred Qualifications

• Experience with Singapore Government platforms such as TechPass, SHIP-HATS, SEED, and GCC.
• Familiarity with Government data classification and security requirements.
• AWS and/or Azure professional certifications.
• Experience designing hybrid on-premises and cloud data architectures.
• Experience with data lineage, metadata management, and data cataloguing solutions.
• Experience supporting enterprise operational systems, asset management, procurement, network, or master data management platforms.