Yiming Peng /i' miŋ/
Senior Data Engineer · PhD in Reinforcement Learning
I design data systems, also fix the leaky "pipes" 😏
- → Currently at Wētā FX, building data infrastructure behind world-class visual effects
- → 10+ years building DataOps/MLOps platforms across New Zealand and global production environments
- → Contributor to Apache Airflow with several merged fixes and improvements
- → Volunteering in the tech community through meetups, talks, and experience-sharing events
Recent
Community & Events
Co-hosting the first Apache Airflow meetup in Wellington, with talks from Chorus and Deloitte.
upcoming · 29 Oct 2026, 5:00-8:30pmTalk on "Become Experienced Before Anyone Hires You" at an IEEE Young Professionals seminar on the future of AI careers.
IEEE Young Professionals panel on transitioning from study and research into industry careers.
Certifications
View all on Credly
Selected Projects
Eliminated recurring pipeline failures; stakeholder teams now rely on it as production-critical infrastructure.
DataOps Platform
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.
Python · Airflow · ClickHouse · Ansible
deep dive comingOpen source work focused on practical, production-informed improvements.
Apache Airflow OSS Contributions
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.
Python · Open Source · Apache Airflow
deep dive comingDramatically reduced data incidents; stakeholders gained confidence to act on data without manual verification.
Data Quality & Observability System
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.
Great Expectations · Python · GitLab CI
deep dive comingFirst production ML infrastructure at the organisation — made model deployment a routine operation rather than a heroic effort.
Kubernetes MLOps Platform
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.
Kubernetes · Kubeflow · AWS EKS
deep dive comingExperience
Publications
12 peer-reviewed papers in reinforcement learning and evolutionary computation (2012-2024) - 165+ citations.