← Lead Bank Interview Insights
Started fine, basically a hashmap keyed on symbol and date.
Start by clarifying requirements: what does 'sensible result' mean for missing data? Then propose a data model using a hash map from stock symbol to a sorted structure (e.g., balanced BST or sorted array) for date-to-price lookup. Discuss time/space trade-offs and how to handle missing dates (e.g., return nearest prior price or null with a clear contract).
Pro tip: In finance, returning the last known price (forward-fill) is often more useful than null, but always confirm with the interviewer—this shows domain awareness and avoids silent assumptions.
Ask about data volume, update frequency, query patterns, and what 'sensible result' means for missing data (e.g., null, error, or nearest prior price).
Propose a map from stock symbol to a time-ordered structure (e.g., balanced BST or sorted array) storing (date, price) pairs for efficient range and point queries.
For a given symbol and date, perform a binary search or tree traversal; if exact date missing, return the most recent prior price (forward-fill) or a sentinel value as agreed.
Discuss time complexity for insertion and lookup (e.g., O(log n) for BST, O(1) for hash map if exact match only) and space complexity, and justify choices.
Address empty store, invalid symbols, future dates, and potential concurrency; mention how to extend to range queries or real-time updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They said 'use whatever formula I give you' and I panicked a little trying to make it generic.
Start by clarifying the requirements: what is the custom formula, what are the date boundaries, and what should happen when a price is missing or the formula fails? Then outline a robust implementation that validates inputs, applies the formula safely with error handling, and ensures atomicity and auditability for financial data.
Pro tip: Emphasize idempotency and audit logging: price changes in banking must be traceable and repeatable without side effects. Mention that you would log the operation details and use transactions to maintain data integrity.
Ask questions to understand the custom formula, date range inclusivity, and expected behavior when prices are missing or the formula fails. Confirm whether the operation should be atomic and if partial updates are allowed.
Define how prices are stored (e.g., time-series table) and validate that both start and end prices exist and are numeric. Check date validity and that the formula is applicable.
Apply the custom formula within a try-catch block, handling division by zero, overflow, or other exceptions. If either price is absent, skip or flag the record based on requirements.
Wrap the operation in a transaction to ensure all-or-nothing updates. Log the operation details (user, timestamp, old/new prices, formula used) for auditing and rollback capability.
Write unit tests for edge cases (missing prices, formula errors, boundary dates) and integration tests. Set up monitoring and alerts for failures in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the requirements and constraints first, then propose a data model and transaction strategy that ensures atomicity and detects conflicts. Discuss trade-offs between pessimistic and optimistic locking, and how to handle concurrency and failure scenarios.
Pro tip: Mention that you would use a database transaction with a unique constraint on (stock_id, date) to enforce atomicity and detect duplicates, and discuss how to handle the error gracefully. This shows you understand both database internals and practical error handling.
Ask about expected concurrency, data volume, and whether the operation must be strictly atomic across all dates. Confirm if partial updates are unacceptable and if the system should support retries.
Propose a table schema with a unique constraint on (stock_id, date) to prevent duplicate updates. Consider adding a version or timestamp column for optimistic concurrency control.
Use a database transaction to update all dates in the range. Before updating, check for existing records for any date in the range; if any exist, rollback and return an error.
Discuss isolation levels (e.g., SERIALIZABLE or REPEATABLE READ) to prevent race conditions. Explain how to handle deadlocks and retries, and ensure idempotency if the operation is retried.
Compare pessimistic locking (SELECT FOR UPDATE) vs optimistic locking (version check). Mention performance implications and scalability concerns for large date ranges.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The pivot to hourly data mid-interview was a curveball.
Clarify the data model and time zone assumptions, then design an efficient query that filters by stock and calendar day, sorts by hour, and returns the results. Discuss indexing and API design to ensure performance and usability.
Pro tip: Mention time zone handling explicitly—storing timestamps in UTC and converting to the user's local time zone for day boundaries shows attention to real-world complexity. Also, consider caching or pre-aggregation if hourly data is frequently accessed.
Ask about time zone handling, data granularity (e.g., are there multiple prices per hour?), and expected volume. Confirm the definition of 'calendar day' (local vs. UTC).
Ensure the store schema supports efficient retrieval by stock and time. Consider a composite index on (stock_id, timestamp) and decide whether to store hourly aggregates or raw ticks.
Write a method that takes stock ID and date, computes the start and end timestamps for that day, filters records, and sorts by timestamp ascending. Handle edge cases like missing hours.
Discuss indexing strategies, query optimization (e.g., using range scans), and potential caching for frequently requested days. Consider pagination if the result set is large.
Specify the method signature, return type (e.g., list of (timestamp, price) pairs), and error handling. Document assumptions about time zone and data completeness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the part I felt most confident about.
Start by clarifying the system under test—likely a financial data ingestion or reporting pipeline—and the expected behavior for each edge case. Then structure your answer around a systematic test design technique (e.g., equivalence partitioning, boundary value analysis) and walk through each edge case with concrete examples. Finally, discuss how you would automate these tests and integrate them into a CI/CD pipeline to catch regressions.
Pro tip: In fintech, edge cases often have regulatory or financial impact—mention how you'd prioritize tests based on risk (e.g., duplicate transactions could cause double-posting) and ensure auditability. Also, show you understand that date/time parsing issues can stem from timezone, DST, and locale differences, not just format mismatches.
Ask questions to understand the system, data flow, and expected behavior for each edge case. Confirm whether the system is batch or real-time, and what 'duplicate' means (exact match vs. business key).
For each edge case, define representative inputs: e.g., duplicate records with same/different timestamps, missing symbols (null, empty, unknown), invalid date ranges (start > end, future dates), repeated updates (same record updated multiple times), and date/time strings in various formats and timezones.
Write specific test cases that cover normal, boundary, and error conditions. For each, specify the input, the action, and the expected result (e.g., duplicate should be rejected or deduplicated, missing symbol should trigger validation error).
Describe how you would implement these tests using a framework (e.g., JUnit, pytest) and data-driven techniques. Include assertions for error messages, logs, and data state. Integrate into CI to run on every commit.
Explain how you would prioritize tests based on risk and likelihood, and how you would report failures with clear reproduction steps. Mention any monitoring or alerting for production edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.