Data Engineer, Trust & Safety

3+ years building data pipelines (batch, stream) in production. Strong in Python, SQL, and data infrastructure (e.g. Kafka, Airflow, Flink, Spark). Experience with data integrity, schema evolution, partitioning, compaction, etc. Deep understanding of performance, latency, indexing. Comfortable designing for failure, retries, backfills, idempotency. Experience in fraud, security, abuse domains. Familiarity with real-time feature serving or feature store systems. Exposure to internal tooling APIs or data mesh architectures.

Build and maintain ETL / streaming pipelines for logs, events, metadata, attribution. Work on aggregation, feature construction, smoothing, and correctness layers. Ensure data quality: alerting, reconciliation, schema validation, drift detection. Optimize for low-latency queries and data access patterns used by enforcement engines and dashboards. Partner with software engineers and analytics to expose data in APIs or internal tools. Instrument logging and observability within your pipelines (monitoring, health dashboards). Help build feature stores or real-time feature services for models or rules.