Data Engineer

Job Summary
The Data Engineer is responsible for designing, building, and maintaining robust data pipelines and infrastructure to support the organization’s data needs. The role involves working with cloud platforms such as AWS, SAS Viya, Alteryx, and Snowflake to ensure data is processed, integrated, and available for analysis and decision-making. The Data Engineer will collaborate closely with stakeholders to enhance data infrastructure, optimize performance, and ensure compliance with data governance policies.

หน้าที่ความรับผิดชอบ (Responsibilities)
Overall Governance
1. Ensure Compliance with Data Policies: Understand and apply data management policies, standards, and procedures in daily tasks, ensuring compliance with legal regulations and alignment with the company’s standards and governance frameworks.
2. Support Policy Revisions: Assist in the periodic review and revision of policies, standards, and procedures related to data engineering to enhance efficiency and ensure alignment with company-wide governance.
3. Promote Ethical Data Use: Ensure the ethical use of data, adhering to relevant guidelines and regulatory requirements, to foster trust and transparency across the organization and with external stakeholders.

Functional-related Accountabilities
1. Stakeholder Coordination: Collaborate with stakeholders, including management and other functions, to identify opportunities for improving data infrastructure and leveraging data engineering practices to address business challenges.
2. Data Management: Identify, validate, and maintain data sources and collection techniques, ensuring the accuracy and integrity of data for processing and analysis.
3. Data Pipeline Development: Design, build, and maintain scalable and reliable data pipelines that support various business applications and analytics requirements.
4. Data Transformation and Integration: Pre-process, transform, and integrate large volumes of structured and unstructured data from various sources, ensuring readiness for analysis and application.
5. Performance Optimization: Implement data optimization techniques to enhance data processing speed and efficiency, ensuring timely delivery of insights and reports.
6. Documentation and Reporting: Prepare comprehensive documentation and reports related to data infrastructure, processes, and analytics outcomes, ensuring clarity and accessibility for stakeholders.
7. Continuous Learning: Stay updated on the latest trends in data engineering, including emerging tools and technologies, and recommend their adoption where beneficial.

Other Accountabilities
1. Team Guidance: Assist in guiding team members in their tasks, providing mentorship to enhance their skills and understanding of data engineering principles.
2. Team Collaboration: Participate in regular team meetings, providing constructive feedback and fostering a collaborative and supportive work environment.
3. Project Support: Support the implementation of data-related projects, ensuring alignment with business objectives and timelines, while assisting in the documentation of progress and performance.
4. Issue Resolution: Identify and address data-related issues, ensuring effective resolution or appropriate escalation to senior management when necessary.
5. Task Delegation: Delegate tasks effectively within the team, ensuring that all projects or assignments are completed on time and meet quality standards.
6. Additional Responsibilities: Take on additional responsibilities as assigned by senior management, demonstrating proactive leadership and initiative.
7. Knowledge Sharing: Encourage and facilitate knowledge sharing within the team on data engineering techniques, tools, and best practices, promoting continuous professional development.