Data Engineer

UST Global; Selangor; On-site; MYR 80,000 - 100,000; Full time

Job summary

A leading global technology service provider is seeking a Data Engineer to design and maintain scalable data systems that support analytics and business intelligence efforts. The ideal candidate will have a strong proficiency in Python and SQL, with at least 5 years of relevant experience. Responsibilities include building data pipelines and architectures, ensuring data quality, and collaborating with stakeholders to drive data-driven decision-making. The role requires familiarity with cloud platforms and data pipeline tools, making it essential for the candidate to possess solid analytical and communication skills.

Qualifications

• Strong proficiency in Python and SQL.
• Hands-on experience with data pipeline tools (e.g., Airflow, DBT, Spark, Kafka).
• Experience working with data lakes, data warehouses, and big data technologies.
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud).
• Understanding of Agile/Scrum methodologies.
• Strong analytical and problem-solving skills.
• Good communication and collaboration abilities.

Responsibilities

• Design, develop, and maintain scalable data models, ETL/ELT pipelines, and data workflows.
• Build and optimize data architectures including data lakes, data warehouses, and big data platforms.
• Develop and maintain data-centric applications and services.
• Integrate structured and unstructured data from multiple sources.
• Ensure data quality, integrity, governance, and security standards are maintained.
• Support and implement cloud-based data solutions (AWS, Azure, or GCP).
• Collaborate with data analysts, BI developers, and business stakeholders.
• Monitor and troubleshoot data pipeline performance and system issues.
• Participate in Agile/Scrum ceremonies and contribute to continuous improvement initiatives.

Skills

Python

SQL

Data pipeline tools (Airflow, DBT, Spark, Kafka)

Data lakes and data warehouses

Cloud platforms (AWS, Azure, Google Cloud)

Agile/Scrum methodologies

Analytical skills

Problem-solving skills

Communication skills

Collaboration abilities

Education

Bachelor’s degree in Information Technology, Computer Science, Engineering, Mathematics

Tools

Airflow

DBT

Spark

Kafka

Job description

The Data Engineer is responsible for designing, developing, and maintaining scalable data systems that power analytics, reporting, and business intelligence initiatives. This role focuses on building reliable data pipelines, managing large-scale data platforms, and enabling data-driven decision-making across the organization.

Key Responsibilities

• Design, develop, and maintain scalable data models, ETL/ELT pipelines, and data workflows
• Build and optimize data architectures including data lakes, data warehouses, and big data platforms
• Develop and maintain data‑centric applications and services
• Integrate structured and unstructured data from multiple sources
• Ensure data quality, integrity, governance, and security standards are maintained
• Support and implement cloud‑based data solutions (AWS, Azure, or GCP)
• Collaborate with data analysts, BI developers, and business stakeholders to deliver reliable datasets
• Monitor and troubleshoot data pipeline performance and system issues
• Participate in Agile/Scrum ceremonies and contribute to continuous improvement initiatives

Required Qualifications

• Strong proficiency in Python and SQL
• Hands‑on experience with data pipeline tools (e.g., Airflow, DBT, Spark, Kafka, etc.)
• Experience working with data lakes, data warehouses, and big data technologies
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud)
• Understanding of Agile/Scrum methodologies
• Strong analytical and problem‑solving skills
• Good communication and collaboration abilities

Education

• Bachelor’s degree in Information Technology, Computer Science, Engineering, Mathematics, or a related field

Experience

• Minimum 5 years of experience in data‑focused software development or data engineering
• Proven experience building scalable and production‑grade data systems

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