I started with the architecture split and that was the right call.
Start by clarifying requirements and constraints, then propose a high-level architecture that covers ingestion, validation, storage, batching, export, and transfer. Dive into each component, emphasizing reliability, idempotency, and security, and discuss trade-offs for key decisions.
Pro tip: Emphasize idempotency and exactly-once semantics in the context of financial systems, and discuss how you would handle partial failures and retries without duplicating data. Also, mention the importance of audit trails and compliance with financial regulations.
Ask questions to understand the volume of transactions, latency requirements, data sensitivity, and specific Mastercard FTP specifications. Clarify the expected failure modes and recovery time objectives.
Outline the main components: ingestion API or message queue, validation service, storage (database and/or object store), batching scheduler, CSV generator, and FTP transfer service. Explain how they interact and scale.
Design the schema for transaction records, including fields for chargeback data, status, timestamps, and idempotency keys. Discuss indexing, partitioning, and retention policies.
Describe the batching strategy (e.g., time-based or size-based), CSV generation, and FTP transfer with retries. Explain how to ensure exactly-once delivery to Mastercard.
Cover error handling, idempotency guarantees, monitoring/alerting, and security measures (encryption, access control, audit logs). Discuss trade-offs and failure recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one I actually had a decent answer for.
Start by defining the batch state after all retries fail and the delivery window is missed, then describe the alerting strategy, and finally explain the idempotent recovery mechanism for the next run. Emphasize how you prevent double-sending and skipping records through transactional state management and idempotency keys.
Pro tip: Mention that you would use a dead-letter queue or a failed state with manual intervention, and that idempotency is enforced at the record level, not just the batch level, to handle partial failures gracefully.
After all retries fail and the delivery window is missed, the batch should transition to a terminal failed state (e.g., FAILED or DEAD_LETTERED) with metadata like failure reason, attempt count, and timestamp.
Trigger alerts to on-call engineers and business stakeholders via channels like PagerDuty, Slack, or email, including batch ID, affected records count, and impact on downstream systems.
Ensure the next run processes only records not yet successfully delivered by using a delivery status flag or idempotency key per record, and by querying the last successful checkpoint.
Implement transactional updates: mark records as 'in-progress' before sending, then 'delivered' upon success; on failure, revert to 'pending' for retry. Use unique message IDs to deduplicate on the receiver side.
Add monitoring for batch success rates and reconciliation jobs to detect and correct any discrepancies between source and destination systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge the distributed systems nature of the problem and propose idempotency as the core solution. Explain how to use a unique idempotency key (e.g., file hash or transaction ID) that both parties can reference to detect duplicates. Then describe the reconciliation process on retry: query Mastercard's status API or check for an acknowledgment before re-uploading.
Pro tip: Emphasize that idempotency must be enforced on both sides—your system and the partner's—and that you should design for failure by persisting the idempotency key before the upload attempt. This shows you think about end-to-end reliability, not just your own service.
Recognize that the crash creates an inconsistent state between your system and Mastercard, and that you need a way to reconcile without double-processing.
Generate a unique key (e.g., UUID, file hash, or business transaction ID) for each upload attempt and persist it before sending. Include this key in the upload request so Mastercard can deduplicate.
On retry, first query Mastercard (or a shared ledger) using the idempotency key to check if the file was already processed successfully. Only re-upload if not found.
Before uploading, record the intent and idempotency key in your database. After upload, update the record with the outcome. This ensures you can recover state after a crash.
Mention scenarios like network timeouts, partial failures, and the need for eventual consistency. Consider using a two-phase commit or saga pattern if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Reconciliation loop: parse the ack file, match rejected records by their IDs back to your DB, flip their status from 'exported' to 'rejected', re-validate them, fix the reason code if possible, and queue them for the next batch.
Start by clarifying the reconciliation process: identify the rejected records, understand the reason code, correct the data, and re-validate them through the same validation pipeline. Then, ensure only the corrected records are re-queued and exported, using idempotent operations and tracking to avoid resending the accepted 97%.
Pro tip: Emphasize idempotency and traceability: use a unique batch ID and record-level status to ensure that re-processing only affects the rejected records, and log every step for auditability. This shows you understand the importance of not duplicating transactions in financial systems.
Query the acknowledgment file to isolate the 3% rejected records, capturing their unique identifiers and the specific reason code for rejection.
Investigate the reason code to determine if it's a data issue, formatting error, or business rule violation, then apply necessary corrections to the records.
Run the corrected records through the same validation logic to ensure they now pass all checks and meet Mastercard's requirements.
Place the validated records into a new batch or queue, ensuring they are distinct from the original accepted records, and export them for submission.
Track the new submission's acknowledgment to confirm the previously rejected records are now accepted, and update internal statuses accordingly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by identifying the likely bottlenecks in each stage of the pipeline as volume grows 10x, then propose independent scaling strategies for ingestion, validation, CSV generation, and transfer. Emphasize decoupling components, using asynchronous processing, and leveraging horizontal scaling to handle increased load.
Pro tip: Demonstrate awareness of trade-offs: for example, scaling ingestion might involve partitioning and backpressure, while CSV generation could use parallel processing and streaming to avoid memory issues. Also, mention monitoring and metrics to detect bottlenecks early.
Analyze each stage (ingestion, validation, CSV generation, transfer) to determine which components will hit limits first as volume grows 10x. Consider CPU, memory, I/O, network, and database constraints.
Propose scaling ingestion by using message queues (e.g., Kafka) to buffer incoming data, partitioning by key, and adding consumer instances. Implement backpressure to handle bursts.
Decouple validation from ingestion by processing asynchronously. Use parallel validation workers, possibly with a stream processing framework (e.g., Flink) to validate in real-time or micro-batches.
Generate CSVs in parallel by partitioning data and using distributed processing (e.g., Spark). Stream CSV writing to avoid memory bottlenecks and store outputs in object storage for scalability.
Use parallel transfers with chunking and retries. Leverage CDNs or multi-threaded uploads/downloads, and consider compression to reduce bandwidth. Ensure transfer is decoupled from generation via queues or storage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.