- Experience: You have 3+ years of experience in Data or Software Engineering, having shipped and operated data pipelines and data services in production.
- Technical mastery: You are strong in Python, and you understand data-specific infrastructure and solutions — how a warehouse, a query engine and an orchestrator actually behave, and which one is the right answer to a given need.
- Tooling expertise: You have run open-source data tools yourself — orchestration, query engines, BI — with Airflow and Snowflake preferred, and you are not afraid to debug the infrastructure under them, where experience with Kubernetes and AWS makes the difference. You are comfortable with Git-based workflows and CI/CD.
- End-to-end ownership: You take a project from a rough need to a shipped solution: you do the product management yourself — challenge the request, scope it with the stakeholder, define what gets built — and then you build it yourself.
- AI fluency: You use AI tools daily in your engineering work (coding agents, LLM-based automation) and you have a clear view of where they help and where they do not.
- Appetite to learn: You are keen to learn a lot, fast, across a broad stack — you see an unfamiliar tool as a reason to dig in rather than a blocker.
- Communication: You have strong communication skills and can translate between business needs and technical constraints, in both directions.
- Languages: You speak fluent English.