# A senior data engineer inherits a fintech with 412 production DAGs Canonical URL: Domain: Pipeline Design ยท Difficulty: medium ## Problem A senior data engineer inherits a fintech with 412 production DAGs. Snowflake credit usage grew 3.5x year-over-year, on-call gets paged 8-12 times per night, three teams compute weekly active users with three different numbers, and an upstream cadence change broke the executive close last month. Apply the entire L4 advanced tier on this canvas: (a-s0) add a catalog node so asset lineage is queryable; (a-s1) make every transform backfill-ready by adding an orchestrator and replacing plain warehouse destinations with lakehouse formats; (a-s2) add an observability_tool + alert_destination on the customer-facing pipelines; (a-s3) split into at least 2 orchestrators for priority isolation between customer-facing and backfill workloads; (a-s4) replace cross-DAG time offsets with lakehouse asset boundaries. ## Related - [All practice problems](https://datadriven.io/problems) - [Mock interview mode](https://datadriven.io/interview/a-senior-data-engineer-inherits-a-fintech-with-412-productio-da532a24) - [System Design Interview Questions](https://datadriven.io/data-engineering-system-design) - [Data Engineering Interview Prep Guide](https://datadriven.io/data-engineer-interview-prep) - [Daily Challenge](https://datadriven.io/daily) --- Source: DataDriven (https://datadriven.io). 100% free data engineering interview prep. Live code execution against Postgres 16, Python 3.11, and Spark sandboxes. No paywall, no premium tier, no signup gate.