Singapore dbt Meetup (in-person) - Oct 2025

dbt Meetups are networking events open to all folks working with data! Talks predominantly focus on community members' experience with dbt, however, you'll catch presentations on broader topics such as analytics engineering, data stacks, data ops, modeling, testing, and team structures.

💜Sponsor: Lightdash
🤝Organizer: Jing Yu Lim, Michael Han (Infinite Lambda), Kenneth Li (foodpanda)
🏠Venue Host: foodpanda
🍕Catering: TBD

To attend, please read the Health and Safety Policy and Terms of Participation: https://www.getdbt.com/legal/health-and-safety-policy

📝Agenda

• 6:30pm - 7:30pm | Food and networking
• 7:30pm - 8:00pm | Presentation by Hamzah (Founder & CEO of Lightdash, the open source BI platform built natively for dbt)
• 8:00pm - 8:30pm | Presentation by foodpanda team

🎤 Presentations

#1 The era of the Analytics Intelligence Engineer

• Speaker: Hazmah Chaudhary (Lightdash, Founder and CEO)
• LinkedIn: https://www.linkedin.com/in/hamzahc
• Synopsis: Back in 2018, dbt paved the way for for this new kind of data professional, people who had technical ability and could understand business context. But here’s the thing: AI is automating traditional tasks like pipeline building and dashboard creation. So then what happens to analytics engineers? They don’t disappear - they evolve.The same skills that made analytics engineers valuable also make them perfect for a new role I’m calling ‘Analytics Intelligence Engineers.’ Instead of writing SQL, they’re writing the context that makes AI actually useful for business users.

#2 Handling row deletions when incrementally updating tables

• Speaker: Hao Ran Lee (foodpanda, Data Engineer)
• LinkedIn: https://www.linkedin.com/in/hao-ran-lee/
• Synopsis: Using incremental loads to update a table has various advantages over using a full load (corresponding to the "table" materialization), but complications arise when we have to account for row deletions in the upstream tables. The existing incremental strategies provided by dbt out-of-the-box lack the capability to take care of row deletions in the right way as well. We'll take a look at several ways to handle row deletions from a dbt perspective, going in depth into one approach that my data team has adopted in our daily operations.

#3 Fragile to Agile: dbt Model Versioning to improve change reliability

• Speaker: Clarence San (foodpanda, Analytics Engineer)
• LinkedIn: https://www.linkedin.com/in/clarencesan/
• Synopsis: Changing a core data model often breaks complex downstream dependencies, forcing a choice between innovation and stability. This session dives into model versioning, a practical framework to tackle this dilemma by using versioning principles to your dbt models. We’ll share our use cases, implementation and learnings.