Build and maintain production-ready data pipelines using DBT, Snowflake, and modern orchestration tools.
Own data engineering features end-to-end, from implementation through optimization and deployment.
Fix and improve existing pipelines
identify bottlenecks, resolve issues, and enhance performance.
Drive automation initiatives across the data stack to accelerate delivery and reduce manual interventions.
Provide 2nd line support for B2B customers
investigate data issues, clarify edge cases, and ensure customers can trust their data.
Design and implement new data import pipelines as we expand our data source coverage.
Implement data quality improvements
validation, monitoring, and testing to ensure reliable, accurate data delivery.
Contribute to code reviews, architectural discussions, and data engineering best practices.

3+ years of professional data engineering experience
Strong fundamentals in SQL, data modeling, Python and ETL/ELT principles
Must have: DBT
hands-on experience building and maintaining transformation pipelines
Nice to have: Snowflake
Databricks
AWS (S3, Lambda, Glue, etc.)
Prefect or similar orchestration tools (Airflow, Dagster)
Solid understanding of data quality principles, testing strategies, and monitoring practices.
Comfortable working in a fast-moving, remote-first environment.

Data Engineer

Key skills