Data Engineer

Role overview Build data infrastructure that makes it easier to integrate customer systems with differing formats, schemas and documentation. The role focuses on a reusable Python ingestion framework and real-time data movement between PostgreSQL and Snowflake, with scope to improve existing systems and reduce manual onboarding work through LLM-assisted schema mapping.

Responsibilities - Develop a reusable Python framework for customer data ingestion, replacing a legacy Java ETL process. - Build and operate streaming and change data capture pipelines to keep PostgreSQL and Snowflake synchronized. - Explore LLM-based schema mapping to reduce manual effort during customer onboarding. - Own production pipelines, including deployment, monitoring, data quality, failure recovery and secure handling of customer data. - Improve existing code and help shape data platform infrastructure and practices.

Requirements - At least 4 years in data engineering, building Python and SQL pipelines for complex integrations. - Experience handling unfamiliar data sources, inconsistent schemas and incomplete documentation. - Practical streaming and CDC experience, including schema changes, backfills and recovery from failures. - Production experience with PostgreSQL, Snowflake, cloud infrastructure (AWS preferred) and Airflow or a similar orchestration tool. - Ability to deploy, monitor and troubleshoot the pipelines you build. - Regular use of AI tools, with concrete examples of applying them to automate tasks or improve development workflows.

Nice to have - Experience with dbt, DuckDB or Terraform. - Familiarity with cross-warehouse data sharing or LLM-assisted data integration.

Benefits and work setup The package includes 25 days of annual leave, private medical insurance and company equity. The role is described as an opportunity to shape practices in a fast-growing environment.