Qualifications:

1. Ultimately strong in Python, SQL, and prompt engineering for developing and optimizing data workflows.

2. Expertise in designing and implementing data warehouses, with a strong focus on real-time streaming architectures.

3. Mastery of dbt (Data Build Tool) for data transformation and modeling.

4. Experience with relational (e.g., MySQL, PostgreSQL) and non-relational (e.g., MongoDB, ArangoDB) databases.

5. Familiarity with DevOps tools like Git, CI/CD pipelines, Docker, Kubernetes, and workflow orchestration systems.

6. Solid understanding of data governance principles, including data quality, security, and stewardship.

7. Exceptional communication skills to interact effectively with technical and non-technical stakeholders.

Preferred Qualifications:

1. Experience in AI/MLOps for service optimization and stability, collaborating closely with AI/DS/MLE teams.

2. Proven track record of delivering AI-powered tools to enhance accuracy and efficiency in workplace or personal tasks.

3. Active participation as a speaker or teacher in data engineering or related fields.

4. Demonstrated leadership/participation in any organization on campus/NGO such as DSI / DEI / FIM / YLI / similar organizations.

5. Experience in developing team members’ careers, such as guiding interns to junior levels or juniors to mid-level roles, or similar situations.

What will you do:

• Supervise data engineering projects from planning to execution, ensuring alignment with business goals, timelines, and quality standards.

• Innovate and implement new data engineering capabilities, such as real-time data pipelines, advanced analytics infrastructure, and enhanced data security.

• Lead the architecture of scalable data warehouses, optimizing for efficiency, reliability, and performance.

• Collaborate with cross-functional teams (engineering, product, marketing, and business units) to understand and address their data needs effectively.

• Drive the development of data pipelines from ingestion to transformation and storage, following ETL/ELT best practices.

• Enforce and maintain data governance frameworks, including access controls, PII data masking, metadata management, and lifecycle management.

• Mentor and develop the skills of data engineers, fostering a collaborative, high-performance culture within the team.

• Continuously evaluate and adopt cutting-edge tools and technologies to enhance the data platform, especially on AI adoption for more efficient and automated working cultures. 

• Ensure the creation and maintenance of comprehensive technical documentation for all data systems and pipelines.