AWS Data Engineer

Cognizant; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

A leading technology firm is seeking a skilled Data Engineer in Singapore. Candidates should have over 3 years of experience in Data Engineering, with a focus on cloud-native services. Proficiency in a coding language like Python, Java, or .NET is required. Knowledge of SQL, data modeling, and experience with AWS or Azure services is essential. This role involves working with various data stores and implementing robust data processing solutions.

Qualifications

• 3+ years of professional experience in Data Engineering.
• At least 2 years focused on cloud-native data services.
• Expert proficiency in one coding language like Python, Java, or .NET.

Skills

SQL Mastery

Data Modeling

Distributed Processing

ETL/ELT Frameworks

Automation

Infrastructure as Code (IaC)

Python

Java

.NET

Tools

Apache Spark (PySpark)

Hadoop

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.