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PadSplit is seeking a hands-on Data Engineer to join its growing analytics platform team. You will work alongside the existing Data Engineering lead to build and maintain robust data ingestion and transformation pipelines. Your primary responsibilities include designing and implementing pipelines using Dagster or Airflow, dbt, and Snowflake, with supporting work in Python, Airbyte, and AWS services.
Key focus areas include:
- Building new data pipelines and dimensional data models with attention to slowly changing dimensions
- Maintaining critical data flows, particularly the daily Postgres → Snowflake → dbt pipeline and third-party API integrations
- Writing production-ready code with thoughtful pull request reviews, clear diffs, and comprehensive test coverage
- Designing idempotent transformations and managing backfill strategies
- Working with AWS infrastructure including task roles, S3 buckets, and cross-account access patterns
You should have strong fundamentals in dimensional modeling, understand the tradeoffs between full refresh and incremental load strategies, and be comfortable reasoning about data quality and failure modes. The role emphasizes collaborative code review practices and moving away from ad-hoc scripts toward maintainable, version-controlled data infrastructure.
This is a fully remote position based in the US, offering the opportunity to shape the data foundation of a growing platform.
