AWS Data Engineer

Cognizant; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading tech company in Singapore is seeking an experienced Data Engineer to work on cloud-native data services. The ideal candidate must have over 3 years in Data Engineering, proficiency in SQL, and hands-on experience with major cloud platforms like AWS or Azure. You'll be responsible for developing complex data pipelines, optimizing data processing, and implementing CI/CD practices. This position offers opportunities to work with cutting-edge data technologies in a dynamic environment.

Qualifications

• 3+ years of professional experience in Data Engineering roles.
• Strong coding skills in Python, Java, or .NET.
• Deep understanding of data structures and modeling.

Responsibilities

• Develop complex SQL queries for data extraction and transformation.
• Build and maintain data pipelines on cloud platforms.
• Implement and oversee data processing frameworks and orchestration tools.

Skills

SQL Mastery

Cloud platform proficiency (AWS/Azure)

Distributed processing (Apache Spark/Hadoop)

CI/CD pipelines

Data modeling

Tools

AWS (S3, RDS, EMR)

Azure (Data Lake, CosmosDB)

Docker

Terraform

Apache Airflow

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.