whoami
Yiming /i' miŋ/ Peng

Production on-premises data platform powering BI and ML workflows across Wētā FX. Built and maintains 25+ ETL pipelines ingesting from disparate sources — solving the persistent problem of unreliable, undocumented data hand-offs between departments.

Impact: Eliminated recurring pipeline failures; stakeholder teams now rely on it as production-critical infrastructure.

Contributor to Apache Airflow — the de facto standard for data pipeline orchestration used by thousands of organizations worldwide. Contributions focus on stability, usability, and operator improvements drawn from real production experience.

Links: GitHub → [PR links — add when available]

Designed and built a platform-wide data quality framework from the ground up using Great Expectations — moving the team from reactive fire-fighting to proactive anomaly detection. Integrated into the CI/CD pipeline so quality checks run automatically on every deploy.

Impact: Dramatically reduced data incidents; stakeholders gained confidence to act on data without manual verification.

Co-designed and built a Kubernetes-based MLOps platform at Chorus NZ to close the gap between data science experimentation and production deployment. Enabled model training, versioning, and serving pipelines within a unified, reproducible infrastructure.

Impact: First production ML infrastructure at the organisation — made model deployment a routine operation rather than a heroic effort.