Data Engineer (AWS, Databricks & Informatica IDMC) | Path Infotech | Singapore

Job Title: Data Engineer (AWS, Databricks & Informatica IDMC) | Path Infotech | Singapore

Recruiting Company: Path Infotech

Job Location: Singapore (Onsite)

Job Type: Full-Time

Additional Information

Onsite Opportunity in Singapore Minimum 5+ Years of Experience Required AWS, Databricks, and Informatica IDMC Expertise Preferred Professional Certifications Highly Valued

Position Summary

Path Infotech is seeking an experienced Data Engineer to build, optimize, and manage enterprise-scale data platforms and pipelines. The successful candidate will play a critical role in delivering high-performance data solutions that support analytics, business intelligence, and digital transformation initiatives across the organization.

Detailed Job Description

As a Data Engineer, you will be responsible for designing, developing, and optimizing modern data architectures in cloud-based environments. You will work closely with business stakeholders, data analysts, and engineering teams to create scalable, reliable, and efficient data pipelines that support complex analytics workloads. The role involves evaluating technical solutions, improving data processing performance, and resolving challenges related to large-scale data transformations. The ideal candidate will possess strong expertise in AWS data services, Databricks, Informatica Intelligent Data Management Cloud (IDMC), and enterprise data integration best practices. This is an excellent opportunity for a data professional looking to contribute to high-impact projects in a fast-paced and innovative environment.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines and enterprise data integration solutions.
• Build and optimize cloud-based data platforms using AWS services and modern data engineering frameworks.
• Implement and manage large-scale data transformation and processing workflows.
• Evaluate technical solutions and recommend improvements for data performance, scalability, and reliability.
• Troubleshoot and resolve complex performance issues involving data pipelines and long-running processes.
• Collaborate with analytics, business intelligence, and application teams to deliver trusted and accessible data.
• Design and maintain robust data models and data integration architectures.
• Monitor data quality, integrity, and governance across data platforms.
• Automate data workflows and promote best practices for operational efficiency.
• Create and maintain technical documentation, standards, and implementation guides.
• Support cloud migration, modernization, and data transformation initiatives.

Required Qualifications & Skills

• Bachelor’s Degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related field.
• Minimum 5 years of hands-on experience in Data Engineering.
• Strong experience working with AWS cloud services and data ecosystems.
• Hands-on experience with Databricks for large-scale data processing and analytics.
• Experience with Informatica Intelligent Data Management Cloud (IDMC).
• Strong knowledge of ETL/ELT architecture, data integration, and pipeline development.
• Experience optimizing data transformation processes and performance tuning.
• Proficiency in SQL and data modeling concepts.
• Experience working with enterprise data warehouses and cloud-native data platforms.
• Strong analytical, troubleshooting, and problem-solving skills.
• Ability to work effectively with cross-functional teams and stakeholders.
• Excellent communication and documentation skills.

Nice-to-Have Skills

• AWS Certified Data Analytics - Specialty certification.
• Databricks Certified Data Engineer certification.
• Informatica professional certifications.
• Experience with real-time data processing and streaming architectures.
• Knowledge of DevOps, CI/CD, and infrastructure automation practices.

Recruitment Pro Tip

For Data Engineering positions, showcase measurable achievements rather than listing technologies alone. Highlight data volumes processed, performance improvements achieved, cloud migration projects completed, cost optimizations delivered, and how your solutions improved analytics, reporting, or business decision-making capabilities.