Problem
Scenario
You're responsible for data pipelines that ingest customer integration data and need a way to spot issues early. Different integrations may arrive through APIs, file drops, or scheduled extracts, so failures can show up as missing data, malformed records, or delayed processing.
Question
How would you design a process to monitor customer integrations and catch failures quickly?
What Can Go Wrong
- Expected file or API payload never arrives
- Data arrives late and misses downstream SLA windows
- Schema changes break parsing or loading
- Volume drops or spikes indicate partial delivery
- Duplicates appear after retries or replay
Monitoring Surfaces
- Ingestion events in TruIQ Data Ingestion Services
- Kafka topic lag and DLQ growth
- Spark validation and processing metrics
- Snowpipe load audit tables in Snowflake
- Curated health dashboards in TruIQ
Practicing as: Data Engineer interview at DevoteamHi, I'll play your Devoteam interviewer for the Data Engineer role. Candidates describe these interviews as mostly positive and moderately difficult, so expect me to be friendly and conversational. Take your time with the question above and answer like we're in the room.
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