EN
721
Data Engineer jobs in Singapore
I want to receive the latest job alerts for “Data Engineer” jobs
Cloud Observability Platform Engineer - Data Infra
ByteDance
Singapore
On-site
SGD 80,000 - 120,000
30+ days ago
Discover more opportunities than anywhere else. Find more jobs nowAnalytics Engineer- Data Operations & Governance
Medium
Singapore
On-site
SGD 70,000 - 90,000
30+ days ago
Connect with headhunters to apply for similar jobsAnalytics Engineer- Data Operations & Governance
Medium
Singapore
On-site
SGD 60,000 - 90,000
30+ days ago
Semiconductor Process Engineer – Data-Driven Problem Solver
Micron Technology, Inc
Singapore
On-site
SGD 60,000 - 85,000
30+ days ago
Senior Full-Stack Engineer — Data Viz & SaaS UI
Looking for a new job?
Singapore
On-site
SGD 70,000 - 90,000
30+ days ago
Data Engineer (1 Year Contract Renewable)
MORGAN MCKINLEY PTE. LTD.
Singapore
On-site
SGD 100,000 - 140,000
30+ days ago
Data Engineer
EXODUSPOINT CAPITAL MANAGEMENT SINGAPORE, PTE. LTD.
Singapore
On-site
SGD 60,000 - 90,000
30+ days ago
IT Support Engineer (Data Center Technician)
TALENTSIS PTE. LTD.
Singapore
On-site
SGD 40,000 - 60,000
30+ days ago
M06 - Data Engineer
FPT ASIA PACIFIC PTE. LTD.
Singapore
On-site
SGD 60,000 - 80,000
Full time
30+ days ago
Job summary
A technology company in Singapore is seeking a Data Engineer to implement and enhance data pipelines, manage data platforms, and engage with stakeholders for data modeling. The ideal candidate has a degree in Computer Science or Engineering, strong programming skills in Python and SQL, and a proactive approach to problem-solving. Experience with cloud technologies like Azure is essential. This role offers the opportunity to work on critical data engineering tasks in a collaborative environment.
Qualifications
- Strong interest in pure Data Engineering work and architecture of data platforms.
- Experience monitoring and managing data pipelines.
- Good communication skills to understand requirements.
Responsibilities
- Support data engineering tasks including data pipeline setup.
- Manage and upgrade data platforms.
- Document use cases and ensure code quality.
Skills
Python
PySpark
SQL
ETL
Azure Data Factory
Docker
Kubernetes
Education
Degree in Computer Science, Engineering, or related disciplines
Tools
PostgreSQL
MS SQL
GeoPandas
JupyterHub
PowerBI
Job description
Data Engineering Work (Key Responsibilities)
- Support data engineering tasks, including the implementation, enhancement, and setup of data pipelines, as well as the rectification of broken pipelines.
- Manage the data platform and perform regular software version upgrades across environments, ensuring thorough testing and clear documentation.
- Support daily operational needs by handling application configuration changes, managing user access, addressing functional queries, and troubleshooting issues.
- Perform source code reviews and configuration reviews periodically to ensure code quality and to verify that sensitive information is not hardcoded or embedded in source code or configuration files.
- Conduct periodic patching of Azure Cloud Servers.
- Engage stakeholders to understand use cases for building new data pipelines, performing data modeling, completing data collection forms, documenting use cases, defining data attributes within various data quality zones, and establishing dataset naming conventions.
- Demonstrate a proactive and diligent approach to issue resolution, improvements, and ownership of data engineering tasks.
- Collaborate effectively within the team and communicate clearly with stakeholders.
Technical Skills
- Programming & Data Manipulation - Python, PySpark, or similar programming languages. Python packages such as Pandas, GeoPandas, Shapely.
- Databases & SQL - Experience with database design and management, e.g. PostgreSQL, MS SQL, Oracle, Geodatabase. Strong SQL programming skills (complex queries, performance tuning, data manipulation).
- ETL & Scripting - ETL (Extract, Transform, Load) experience. Scripting and version control (Bash, PowerShell, Git).
- Analytics & Tools - Technologies such as JupyterHub, RStudio, PowerBI. CI/CD tools such as Jenkins, GitLab, YAML.
- Integration & Cloud - API development and SFTP for secure data transfer. Cloud Platforms: Microsoft Azure, AWS. Cloud Technologies: Azure Data Factory, Databricks, Azure Functions, Azure Key Vault, AWS Lambda.
- Containerization - Experience with Docker, Kubernetes (added advantage).
Requirements
- Degree in Computer Science, Engineering, or related disciplines.
- Strong interest in pure Data Engineering work (not AI research or Business Analytics roles).
- Experienced in data engineering, including:
- Implementation and enhancement of data pipelines
- Development of data models and schemas
- Pipeline monitoring and management
- Setting up data pipelines on Azure, Databricks, and using Python scripting
- Experienced in architecting, designing, and developing data platforms.
- Good communication skills and ability to quickly understand technical and business requirements.
- Proactive, diligent, and able to work independently as well as collaboratively.
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.