M18 - Data Engineer at FPT Asia Pacific Pte Ltd

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

17 Aug, 26

Salary

0.0

Posted On

19 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, VQL, AWS Glue, AWS Athena, AWS S3, AWS RDS, AWS SageMaker, Denodo, Data Modelling, ETL, CI/CD, GitLab, Agile, RESTful APIs, Data Governance

Industry

IT Services and IT Consulting

Description

Role Overview We are looking for experienced Data Engineers to support the development and maintenance of enterprise data products within the organisation's data platform. The role involves designing scalable data solutions, building data pipelines, integrating enterprise systems, and enabling data-driven decision-making across the organisation. Key Responsibilities Data Engineering & Platform Integration Design, develop, and maintain data pipelines and ETL processes using AWS services such as Glue, Athena, S3, and RDS Develop and manage data virtualisation solutions using Denodo and VQL Ingest and process data from internal and external data sources Perform data extraction, cleansing, transformation, and loading activities Implement automated data collection and API integration processes Data Architecture Design and implement conceptual, logical, and physical data models using tools such as ER Studio Develop and maintain data warehouses, data lakes, and operational data stores Develop and maintain enterprise data blueprints Create data marts and analytical views to support business intelligence and reporting needs Implement data governance standards and master data management practices Technical Architecture & Integration Ensure seamless integration across various enterprise data systems and applications Implement data security, compliance, and governance requirements Design scalable solutions for enterprise data integration and consolidation Development & Analytics Develop Python scripts in AWS Glue for data processing and automation Write and optimise VQL/SQL queries and stored procedures Design and develop RESTful APIs for data services using modern frameworks and best practices Work with AWS SageMaker for machine learning model deployment and integration Optimise database performance through indexing, query tuning, and maintenance Participate in Agile ceremonies including sprint planning, stand-ups, and retrospectives Implement and maintain CI/CD pipelines for automated testing and deployment Participate in peer code reviews and collaborative development practices Documentation & Best Practices Create and maintain technical documentation for data models, systems, and processes Follow coding standards, version control, and change management best practices Stakeholder Collaboration Collaborate with cross-functional teams on data engineering initiatives Gather requirements, conduct technical discussions, and implement scalable data solutions Work closely with Product Managers, Business Analysts, Data Analysts, Solution Architects, and UX Designers Provide technical guidance and support for data-related matters Requirements At least 3 years of experience in Data Engineering or a related role Strong proficiency in Python, SQL, and VQL Hands-on experience with AWS services such as Glue, Athena, S3, RDS, and SageMaker Experience with data virtualisation tools, preferably Denodo Familiarity with BI tools such as Tableau or Power BI Good understanding of data modelling and database design principles Knowledge of data governance and master data management concepts Experience with GitLab, version control practices, and CI/CD pipelines Experience working in Agile development environments Strong analytical, problem-solving, and communication skills Ability to work effectively in a collaborative team environment Nice to Have Knowledge of AI technologies such as AWS Bedrock, Azure AI, and Large Language Models (LLMs)

Responsibilities

Design and maintain scalable data pipelines and ETL processes using AWS services and Denodo data virtualization. Develop data models, manage data lakes, and implement data governance standards to support enterprise decision-making.