Someone with 5+ years of data engineering experience who has worked in a production environment with real pipelines. A person who understands the full data lifecycle from ingestion, through transformation, to serving data to the business. Able to communicate in English with the Client (the role requires direct contact with Customer). Self-driven, proactive, and not afraid to make technical decisions.
requirements-expected :
Strong Python - Were not looking for someone who writes scripts - we need clean, testable, production-grade code. Understanding of project structure, dependency management, testing, and error handling is expected.
Orchestration — must have - Production experience with at least one orchestrator: Prefect is preferred, but Airflow or Dagster are also acceptable. It must be hands-on production experience, not just a course or certification. The candidate should understand how to design pipelines, handle errors, implement retry logic, and manage task dependencies.
Version Control with GitHub - Working knowledge of branches, pull requests, and code reviews. Experience with GitHub Actions and CI/CD is a strong plus.
Containerization - Practical Docker skills — writing Dockerfiles, building images, understanding layers. Kubernetes at least conceptually, ideally with production experience.
dbt — beyond just knowing the name - Understanding of how a dbt project works from the inside: layer structure (staging - intermediate - marts), configuration files (dbt_project.yml, sources, schema), macros, and tests. The candidate should be able to design and build a transformation layer.
SQL at a solid level - Writing SELECTs is not enough. Were looking for someone who understands stored procedures, how to manage schema access (GRANT, REVOKE, roles), materialized views, window functions, and data modeling.
