10x data engineer with dlt+ and Tower: A Taktile Case Study

Real-World efficiency gains with dlt+ and Tower: A Taktile demo

As a senior data engineer, you know the drill: too often, “scaling data” means endless configuration, dependency headaches, and painfully slow onboarding for new team members. Taktile’s recent demo, presented by Simon, shows a different path: one where just about anyone with some Python skills can contribute directly to production-grade data pipelines in under an hour.

They achieved this by combining two key technologies:

• dlt+ (from dltHub): A Python-native, open-source framework that unifies ingestion, transformation, testing, and schema enforcement. It’s flexible enough to tie into almost any environment yet structured enough to maintain data governance and prevent chaos. Join our waiting list now!
• Tower: A portable Python runtime that makes it trivial to share secrets, align dependencies, and seamlessly move from local dev to production. Instead of wrestling with CI/CD pipelines or environment mismatch, you just “tower run” and go. Read more on their blog.

In Taktile’s scenario, this approach turned a two-person data bottleneck into a true self-service model. A small fix, like adding a priority field in a Jira or Linear data pipeline, no longer demands a data engineer’s constant attention. With Tower and dlt+, a project manager, or any colleague comfortable with Python, can quickly modify the pipeline and ship changes. This frees up the core data team for higher-value, strategic work.

View the video here:

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