Data Engineer

Mô tả Công việc
The Data Engineer is responsible for building, integrating, and operating the company's data platform, ensuring that data is collected, processed, stored, and delivered accurately, securely, and in a timely manner.
This position will also participate in researching and implementing emerging technologies related to API Integration, Data Platforms, Dashboards, AI Agents, and AI Automation. The role aims to automate business processes, improve operational efficiency, and support data-driven decision-making across the organization.
Key Responsibilities
1. System Integration and API Development
· Analyze, design, and develop integration solutions connecting ERP, CRM, internal applications, partner systems, and third-party platforms.
· Build, manage, and optimize RESTful APIs, webhooks, data synchronization services, and system integration workflows.
· Ensure the accuracy, integrity, and consistency of data exchanged between systems.
· Monitor integration workflows, troubleshoot incidents, and implement logging and alerting mechanisms.
2. Data Platform Development and Operations
· Design and develop ETL/ELT processes to collect, clean, transform, standardize, and consolidate data.
· Build and operate Data Warehouses, Data Lakes, or centralized data platforms for reporting and analytics.
· Design data structures, data models, and Data Marts aligned with business requirements.
· Optimize query performance, data storage, and processing efficiency.
· Develop data quality validation mechanisms, maintain data history, and identify and handle data anomalies.
· Monitor and maintain Data Pipelines to ensure stability, reliability, and scalability.
3. Dashboards and Business Intelligence
· Collaborate with business departments to understand reporting requirements and management KPIs.
· Build datasets, semantic models, and dashboards to support operational management and executive decision-making.
· Develop management reports using Power BI or equivalent Business Intelligence platforms.
· Ensure that reporting data is accurate, consistent, and traceable to its original sources.
· Optimize data refresh processes and dashboard performance.
4. Internal Application and Tool Development
· Develop applications, services, and internal tools that support data management, operations, and analytics.
· Build backend services, API services, and automation programs to improve operational efficiency.
· Participate in requirement analysis, solution design, development, testing, deployment, and maintenance of new technology solutions.
· Collaborate with Business Analysts, Developers, AI Engineers, and business stakeholders throughout the implementation process.
5. AI Agents and AI Automation
· Research, evaluate, and implement AI Agents in suitable business processes.
· Develop AI Automation workflows that connect data sources, APIs, internal systems, and AI models.
· Prepare, process, and provide data for AI applications, chatbots, Retrieval-Augmented Generation systems, and intelligent analytics solutions.
· Integrate AI platforms such as OpenAI, Google Gemini, Anthropic, or internally deployed AI models.
· Evaluate the effectiveness, accuracy, cost, scalability, and security risks of AI solutions.
· Propose AI initiatives that reduce manual work, streamline processes, and improve employee productivity.
6. Governance, Security, and Documentation
· Implement role-based access control and data authorization mechanisms.
· Ensure that data and integration information are protected throughout the entire processing lifecycle.
· Prevent sensitive data from being exposed through APIs, dashboards, system logs, or AI services.
· Develop and maintain technical documentation, API documentation, Data Dictionaries, Data Pipelines, AI workflows, and operational guidelines.
· Perform source-code version control, testing, and deployment in accordance with the Information Technology department's processes and standards.