Build Idempotent Payment API Pipeline | Dataford Interview Questions

Context

PayFlow, a B2B payments platform, currently receives charge requests from web and mobile clients through a synchronous API backed by PostgreSQL. During client retries, network timeouts, and Airflow-triggered replay jobs, the system occasionally creates duplicate payment records and downstream settlement events.

You need to design an idempotent API ingestion pattern that guarantees a payment request is processed once per client-supplied idempotency key while still supporting retries, auditability, and downstream ETL into the analytics warehouse.

Scale Requirements

Traffic: 2,500 API requests/second peak, 400 requests/second average
Payload size: 1-4 KB JSON per payment request
Latency target: API response p95 < 250 ms
Duplicate prevention window: 24 hours per idempotency key
Storage: 50M payment requests/day, 2 TB/month raw + audit logs
Downstream freshness: analytics tables updated within 5 minutes

Requirements

Implement an API endpoint that accepts an Idempotency-Key header and safely handles client retries.
Persist request state so the same key returns the original response instead of creating a second transaction.
Prevent race conditions when two identical requests arrive concurrently.
Publish successful payments to the event pipeline for downstream ETL and reconciliation.
Store raw requests, processing status, and response payloads for audit and replay.
Add data quality checks to detect duplicate transactions, mismatched payloads for reused keys, and missing downstream events.
Describe how orchestration would backfill failed warehouse loads without re-charging customers.

Constraints

AWS-based stack only; no multi-region active-active requirement
PCI-sensitive fields must be tokenized; raw card data cannot be stored in logs or Kafka
Incremental infrastructure budget: $15K/month
Existing team uses Python, Airflow, PostgreSQL, and Kafka; avoid introducing niche tooling
Payment records must be retained for 7 years for audit

Problem

Context

Scale Requirements

Traffic: 2,500 API requests/second peak, 400 requests/second average
Payload size: 1-4 KB JSON per payment request
Latency target: API response p95 < 250 ms
Duplicate prevention window: 24 hours per idempotency key
Storage: 50M payment requests/day, 2 TB/month raw + audit logs
Downstream freshness: analytics tables updated within 5 minutes

Requirements

Implement an API endpoint that accepts an Idempotency-Key header and safely handles client retries.
Persist request state so the same key returns the original response instead of creating a second transaction.
Prevent race conditions when two identical requests arrive concurrently.
Publish successful payments to the event pipeline for downstream ETL and reconciliation.
Store raw requests, processing status, and response payloads for audit and replay.
Add data quality checks to detect duplicate transactions, mismatched payloads for reused keys, and missing downstream events.
Describe how orchestration would backfill failed warehouse loads without re-charging customers.

Constraints

AWS-based stack only; no multi-region active-active requirement
PCI-sensitive fields must be tokenized; raw card data cannot be stored in logs or Kafka
Incremental infrastructure budget: $15K/month
Existing team uses Python, Airflow, PostgreSQL, and Kafka; avoid introducing niche tooling
Payment records must be retained for 7 years for audit

Problem

Context

Scale Requirements

Traffic: 2,500 API requests/second peak, 400 requests/second average
Payload size: 1-4 KB JSON per payment request
Latency target: API response p95 < 250 ms
Duplicate prevention window: 24 hours per idempotency key
Storage: 50M payment requests/day, 2 TB/month raw + audit logs
Downstream freshness: analytics tables updated within 5 minutes

Requirements

Implement an API endpoint that accepts an Idempotency-Key header and safely handles client retries.
Persist request state so the same key returns the original response instead of creating a second transaction.
Prevent race conditions when two identical requests arrive concurrently.
Publish successful payments to the event pipeline for downstream ETL and reconciliation.
Store raw requests, processing status, and response payloads for audit and replay.
Add data quality checks to detect duplicate transactions, mismatched payloads for reused keys, and missing downstream events.
Describe how orchestration would backfill failed warehouse loads without re-charging customers.

Constraints

AWS-based stack only; no multi-region active-active requirement
PCI-sensitive fields must be tokenized; raw card data cannot be stored in logs or Kafka
Incremental infrastructure budget: $15K/month
Existing team uses Python, Airflow, PostgreSQL, and Kafka; avoid introducing niche tooling
Payment records must be retained for 7 years for audit

Problem

Context

Scale Requirements

Traffic: 2,500 API requests/second peak, 400 requests/second average
Payload size: 1-4 KB JSON per payment request
Latency target: API response p95 < 250 ms
Duplicate prevention window: 24 hours per idempotency key
Storage: 50M payment requests/day, 2 TB/month raw + audit logs
Downstream freshness: analytics tables updated within 5 minutes

Requirements

Implement an API endpoint that accepts an Idempotency-Key header and safely handles client retries.
Persist request state so the same key returns the original response instead of creating a second transaction.
Prevent race conditions when two identical requests arrive concurrently.
Publish successful payments to the event pipeline for downstream ETL and reconciliation.
Store raw requests, processing status, and response payloads for audit and replay.
Add data quality checks to detect duplicate transactions, mismatched payloads for reused keys, and missing downstream events.
Describe how orchestration would backfill failed warehouse loads without re-charging customers.

Constraints

AWS-based stack only; no multi-region active-active requirement
PCI-sensitive fields must be tokenized; raw card data cannot be stored in logs or Kafka
Incremental infrastructure budget: $15K/month
Existing team uses Python, Airflow, PostgreSQL, and Kafka; avoid introducing niche tooling
Payment records must be retained for 7 years for audit