Data Engineer (Fully Remote)
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PadSplit is growing its analytics platform and needs a hands-on Data Engineer to work alongside our existing DE lead. The role combines building new pipelines and data models with providing real coverage on critical paths.
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The Role We Need PadSplit is growing its analytics platform and needs a hands-on Data Engineer to work alongside our existing DE lead. This person will build and maintain ingestion and transformation pipelines across Dagster (or Airflow), dbt, and Snowflake, with supporting work in Python, Airbyte, and AWS. The role combines building new pipelines and data models with providing real coverage on critical paths — especially the daily Postgres → Snowflake → dbt flow and third-party API loads — all shipped through reviewed pull requests rather than one-off scripts. The Person We Are Looking For We're looking for a practitioner who thinks natively in dimensions, facts, and slowly changing dimensions — someone who knows when to use a full refresh versus an incremental load and how that choice affects idempotency and backfills. This person writes clear, reviewable PRs and gives equally thoughtful reviews, with attention to scoped diffs, sensible tests, and failure modes. They're comfortable in complex Python data flows and have enough AWS literacy to reason about task roles, buckets, and cross-account access without needing to own all of platform engineering.
Чем предстоит заниматься
- Build and maintain ingestion and transformation pipelines across Dagster (or Airflow), dbt, and Snowflake
- Provide real coverage on critical paths — especially the daily Postgres → Snowflake → dbt flow and third-party API loads
- Ship all work through reviewed pull requests rather than one-off scripts
Что требуется
- Thinks natively in dimensions, facts, and slowly changing dimensions
- Knows when to use a full refresh versus an incremental load and how that choice affects idempotency and backfills
- Writes clear, reviewable PRs and gives equally thoughtful reviews, with attention to scoped diffs, sensible tests, and failure modes
- Comfortable in complex Python data flows
- Has AWS literacy to reason about task roles, buckets, and cross-account access without needing to own all of platform engineering
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