Ultimately strong in Python, SQL, and prompt engineering for developing and optimizing data workflows.
Expertise in designing and implementing data warehouses, with a strong focus on real-time streaming architectures.
Mastery of dbt (Data Build Tool) for data transformation and modeling.
Experience with relational (e.g., MySQL, PostgreSQL) and non-relational (e.g., MongoDB, ArangoDB) databases.
Familiarity with DevOps tools like Git, CI/CD pipelines, Docker, Kubernetes, and workflow orchestration systems.
Solid understanding of data governance principles, including data quality, security, and stewardship.
Exceptional communication skills to interact effectively with technical and non-technical stakeholders.

Preferred Qualifications:

Experience in AI/MLOps for service optimization and stability, collaborating closely with AI/DS/MLE teams.
Proven track record of delivering AI-powered tools to enhance accuracy and efficiency in workplace or personal tasks.
Active participation as a speaker or teacher in data engineering or related fields.
Demonstrated leadership/participation in any organization on campus/NGO such as DSI / DEI / FIM / YLI / similar organizations.
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.