AWS Data Engineer

ENFACTUM PTE. LTD.; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading data engineering firm in Singapore is seeking a Data Engineer with 3+ years of experience focused on cloud-native services. The ideal candidate will be proficient in one coding language and have solid SQL mastery. Responsibilities include building data transformations, utilizing cloud services, and architecting scalable data systems. This role offers a dynamic environment to contribute to innovative data solutions.

Qualifications

• 3+ years of experience in Data Engineering roles, 2 years focused on cloud-native services.
• Expert proficiency in one coding language, like Python, Java, or .NET.
• Understanding of data structures and data access patterns.

Responsibilities

• Build and maintain data transformations.
• Utilize cloud services for data engineering.
• Architect robust and scalable data systems.

Skills

Cloud-native data services

SQL mastery

Data modeling

Data engineering

Apache Spark

ETL/ELT frameworks

Tools

AWS

Azure

Git

Docker

Terraform

Job description

• Experience: 3+ years of professional experience in Data Engineering roles, with at least 2 years focused on cloud-native data services.
• Programming Expertise: Expert proficiency in one coding language like Python , Java or .NET.
• Data Fundamentals:○SQL Mastery: Solid expertise in writing complex and highly optimized

SQL queries for relational databases and data warehouses.

• Data Modeling: Deep understanding of data structures, data modeling (e.g., dimensional modeling), and data access patterns.
• Diverse Data Stores: Experience working with a variety of databases, including relational (e.g., PostgreSQL, MySQL) , NoSQL (e.g., DynamoDB, CosmosDB) , and distributed file systems.
• Cloud Proficiency (Practical Tooling): Proven hands‑on experience in at least one major cloud platform, utilizing services critical to data engineering.
• AWS Examples: S3, RDS/Aurora, EMR, Glue, Athena, Redshift, Lambda.
• Azure Examples: Data Lake Storage, Azure SQL, CosmosDB, Azure Data Factory, Synapse.
• Pipeline & Processing:○Distributed Processing: Extensive experience with Big Data/distributed data processing frameworks like Apache Spark (PySpark) or Hadoop.○ETL/ELT Frameworks: Strong experience building and maintaining data transformations using frameworks like PySpark and libraries like Pandas .
• Orchestration: Experience with modern workflow orchestration tools such as Apache Airflow or Azure Data Factory .
• DevOps & Governance: Automation: Familiarity with building and using CI/CD pipelines for automated deployment.
• Infrastructure as Code (IaC): Experience with DevOps tools such as Git, Docker, and Terraform

System Design: Understanding of system design principles and experience in architecting robust, scalable, and secure data systems.