Python development for data engineering, ETL pipelines, ML systems, and automation. The language our data and AI work runs on, in production daily.
Notebook Python versus production Python
Python is easy to start and easy to abandon half-finished: untyped scripts, hardcoded credentials, no tests, one person who understands the cron job. That works until the data matters. Production Python needs the same rigor as any backend language, and that rigor is what we bring.
What we build with Python
- ETL and ELT pipelines pulling from ad platforms, marketplaces, and internal systems
- API integrations with retry, throttling, and idempotency built in
- ML and LLM application backends with evaluation baked in
- Automation services that replace recurring manual work
How we work
Type hints, tests, and CI as the default, not an upgrade
Structured logging and alerting on every scheduled job
Package and deploy like software, not like scripts
Document data contracts so downstream users can trust outputs
Typical stack
Frequently asked questions
For orchestration and transformation, yes, since heavy lifting happens in the warehouse or in native libraries. Where raw throughput dominates we pair Python orchestration with Go or Rust workers.
Yes, that is common. We start with an audit, add tests around the riskiest paths, and improve incrementally while the system keeps running.