← Boston Consulting Group Interview Insights
The dedup part sounds trivial until you realize there are near-duplicate rows sharing the same txn_id and dup_hint but different updated_at values.
Start by clarifying the data schema, timestamp formats, and validation rules, then outline a vectorized pandas pipeline that uses merge, sort_values, drop_duplicates, and boolean masks instead of apply or loops. Emphasize scalability by discussing chunking, dtype optimization, and memory-efficient operations for 100M+ rows.
Pro tip: Mention that you would validate the pipeline on a small sample and use pandas' nullable dtypes and categorical types to reduce memory, and consider using Dask or Polars if pandas becomes a bottleneck.
Ask about CSV structure, timestamp formats, timezone info, valid transaction types, amount ranges, and the defined timestamp range. Confirm whether user data is needed for joins or just for reference.
Outline steps: read CSVs with appropriate dtypes, parse timestamps to UTC, merge user data if needed, deduplicate by sorting and drop_duplicates, and apply boolean masks for validation. Avoid apply and loops.
Use sort_values(['txn_id', 'updated_at'], ascending=[True, False]) followed by drop_duplicates('txn_id', keep='first') to keep latest. Create masks for invalid amounts, types, and timestamps using vectorized comparisons.
Discuss chunked reading, dtype downcasting (e.g., float32, category), and using efficient file formats like Parquet. Mention alternatives like Dask or Polars for out-of-core processing.
Describe how to test on a small sample, check for edge cases (e.g., NaT, duplicates with same updated_at), and ensure the output meets business rules.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the transaction data schema and business definitions of refunds and chargebacks. Then design a query that aggregates purchase amounts separately while using conditional logic to flag any refund or chargeback activity per user. Finally, validate the results and discuss edge cases like multiple refunds or partial refunds.
Pro tip: Mention that refunds and chargebacks should be excluded from net_spend but their presence is still valuable for segmentation, and consider using a LEFT JOIN or window function to efficiently compute the flag without duplicating rows.
Confirm the transaction table structure, how refunds and chargebacks are recorded (e.g., negative amounts, separate type column), and the time window for analysis.
Filter the transaction data to include only purchase records, ensuring refunds and chargebacks are excluded from the net_spend calculation.
Aggregate the purchase amounts by user to calculate net_spend as the sum of purchase amounts only.
Use a conditional aggregation or window function to set a flag (e.g., 1/0) indicating whether the user has any refund or chargeback transactions.
Check for users with only refunds/chargebacks (net_spend = 0), partial refunds, and ensure the flag correctly captures any occurrence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data schema and timezone handling, then outline a pipeline that first assigns each transaction to a local month, converts timestamps to UTC for aggregation, and computes features per user-month. Emphasize the rolling median calculation over active months only, and discuss trade-offs between window functions and self-joins.
Pro tip: Mention that you would validate the timezone conversion by checking edge cases like transactions near month boundaries and users in different timezones, and consider using a calendar table to ensure all user-month combinations are represented before filtering to active months.
Confirm the definition of 'active month' (e.g., at least one transaction), the five-month window (e.g., last five complete months), and the source of user timezone. Identify the transaction table with user_id, timestamp, amount, and timezone.
For each transaction, convert the timestamp to the user's local timezone to extract the local month, then convert the timestamp to UTC for aggregation. Ensure consistent handling of daylight saving time if applicable.
Group by user_id and local month to compute total net spend (sum of amounts) and determine active_month (1 if any transaction, else 0). Note that inactive months will have no rows, so they must be handled later.
For each user, consider only months where active_month=1. For each active month, compute the median of monthly_net_spend over the current and previous two active months (if available). Use window functions or a self-join to achieve this without zero-filling.
Check for users with fewer than three active months, missing months, and timezone edge cases. Ensure the output includes all user-month combinations for the five-month window, with inactive months having active_month=0 and null or zero spend as appropriate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I used nlargest with a sort_values fallback for the lexicographic tiebreak and then pivoted.
Start by aggregating net spend per user-month-category, then rank categories within each group using a deterministic ordering: descending net spend, then ascending category name. Finally, pivot the top three into cat1, cat2, cat3 columns, filling missing slots with 'None'.
Pro tip: Explicitly state how you handle ties and missing categories, and mention that you would validate the output with edge cases like users with zero or one category to ensure robustness.
Compute net spend per user, month, and merchant category by summing transaction amounts (or net of refunds).
Within each user-month, rank categories by net spend descending, then by category name ascending to break ties.
Filter to the top three ranked categories per user-month, ensuring you capture the correct order.
Transform the ranked list into cat1, cat2, cat3 columns, using 'None' for any missing positions.
Check for correctness by testing edge cases (e.g., users with fewer than 3 categories) and verifying tie-breaking logic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The idempotency requirement is mostly about making sure your dedup and sort logic is deterministic, which it is if you sort by updated_at and break ties consistently.
Start by clarifying the requirements and assumptions, then design a modular pipeline that processes data incrementally and computes each metric efficiently. Focus on idempotency by using deterministic transformations and overwrite/merge strategies, and on memory efficiency by leveraging window functions and partitioning. Finally, discuss trade-offs and validation steps.
Pro tip: Emphasize partitioning by user and date to enable incremental processing, and use approximate algorithms (e.g., t-digest) for median calculations when exactness isn't critical. Also, mention the importance of handling late-arriving data and timezone consistency.
Ask questions to confirm definitions: what counts as 'active'? How is net spend calculated (refunds subtracted)? What defines a refund/chargeback flag? Confirm snapshot date and timezone.
Outline a star schema with fact tables for transactions and refunds, and dimension tables for users and merchants. Propose a batch pipeline with incremental processing, partitioning by date and user.
Use window functions for rolling calculations (e.g., months active, rolling median) and aggregations for net spend and top categories. For median, consider approximate algorithms if data volume is large.
Implement idempotency via deterministic transformations and overwrite/merge on primary keys. Optimize memory by processing in chunks, using columnar storage, and avoiding unnecessary shuffles.
Define data quality checks (e.g., counts, nulls, ranges) and compare against expected results. Set up monitoring for pipeline failures and performance metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.