Data Engineer

EXASOFT PTE. LTD.; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

A data engineering company in Singapore is seeking an experienced Data Engineer to design and optimize data storage solutions. The ideal candidate will have over 5 years of experience with AWS services and data engineering, proficient in Python and SQL, and must be capable of managing complex data pipelines. This role offers a dynamic environment and the opportunity to work with cutting-edge technologies.

Qualifications

• Minimum 5 years of experience in data engineering.
• Strong knowledge of SQL and NoSQL databases.
• AWS certifications are a plus.
• Familiarity with data visualization tools.

Responsibilities

• Design and architect data storage solutions using AWS services.
• Create, manage, and optimize data pipelines for ingestion and processing.
• Develop ETL processes to cleanse and enrich data.
• Implement security best practices for data protection.
• Collaborate with cross-functional teams to understand data requirements.

Skills

AWS services

Databricks

Informatica IDMC

Python

Java

Scala

SQL

NoSQL databases

Data modeling

Problem-solving

Communication

Collaboration

Education

Bachelor’s or master’s degree in computer science, data engineering, or a related field

Tools

AWS Glue

AWS Data Pipeline

AWS Lambda

Databricks for data processing

SQL databases

Apache Spark

Hadoop

Docker

Kubernetes

Tableau

Power BI

Git

Job description

Responsibilities

• Design and architect data storage solutions, including databases, data lakes, and warehouses, using AWS services such as Amazon S3, Amazon RDS, Amazon Redshift, and Amazon DynamoDB, along with Databricks' Delta Lake. Integrate Informatica IDMC for metadata management and data cataloging.
• Create, manage, and optimize data pipelines for ingesting, processing, and transforming data using AWS services like AWS Glue, AWS Data Pipeline, and AWS Lambda, Databricks for advanced data processing, and Informatica IDMC for data integration and quality.
• Integrate data from various sources, both internal and external, into AWS and Databricks environments, ensuring data consistency and quality, while leveraging Informatica IDMC for data integration, transformation, and governance.
• Develop ETL (Extract, Transform, Load) processes to cleanse, transform, and enrich data, making it suitable for analytical purposes using Databricks' Spark capabilities and Informatica IDMC for data transformation and quality.
• Monitor and optimize data processing and query performance in both AWS and Databricks environments, making necessary adjustments to meet performance and scalability requirements. Utilize Informatica IDMC for optimizing data workflows.
• Implement security best practices and data encryption methods to protect sensitive data in both AWS and Databricks, while ensuring compliance with data privacy regulations. Employ Informatica IDMC for data governance and compliance.
• Implement automation for routine tasks, such as data ingestion, transformation, and monitoring, using AWS services like AWS Step Functions, AWS Lambda, Databricks Jobs, and Informatica IDMC for workflow automation.
• Maintain clear and comprehensive documentation of data infrastructure, pipelines, and configurations in both AWS and Databricks environments, with metadata management facilitated by Informatica IDMC.
• Collaborate with cross-functional teams, including data scientists, analysts, and software engineers, to understand data requirements and deliver appropriate solutions across AWS, Databricks, and Informatica IDMC.
• Identify and resolve data-related issues and provide support to ensure data availability and integrity in both AWS, Databricks, and Informatica IDMC environments.
• Optimize AWS, Databricks, and Informatica resource usage to control costs while meeting performance and scalability requirements.
• Stay up-to-date with AWS, Databricks, Informatica IDMC services, and data engineering best practices to recommend and implement new technologies and techniques.

Requirements

• Bachelor’s or master’s degree in computer science, data engineering, or a related field.
• Minimum 5 years of experience in data engineering, with expertise in AWS services, Databricks, and/or Informatica IDMC.
• Proficiency in programming languages such as Python, Java, or Scala for building data pipelines.
• Evaluate potential technical solutions and make recommendations to resolve data issues especially on performance assessment for complex data transformations and long running data processes.
• Strong knowledge of SQL and NoSQL databases.
• Familiarity with data modeling and schema design.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.
• AWS certifications (e.g., AWS Certified Data Analytics - Specialty, AWS Certified Data Analytics - Specialty), Databricks certifications, and Informatica certifications are a plus.
• Experience with big data technologies like Apache Spark and Hadoop on Databricks.
• Knowledge of containerization and orchestration tools like Docker and Kubernetes.
• Familiarity with data visualization tools like Tableau or Power BI.
• Understanding of DevOps principles for managing and deploying data pipelines.
• Experience with version control systems (e.g., Git) and CI/CD pipelines.
• Knowledge of data governance and data cataloguing tools, especially Informatica IDMC.