← SIG (Susquehanna) Interview Insights
Straightforward on the surface but the 'regardless of when delivered' part tripped me up briefly.
Start by clearly defining the 7-day window filter on the 'created_at' timestamp to scope the order cohort, then aggregate delivered orders (status = 'delivered' AND delivered_at IS NOT NULL) grouped by city and courier_type. Express the conversion rate as COUNT(delivered orders) / COUNT(total created orders) per group, handling potential division-by-zero edge cases.
Pro tip: Explicitly call out that 'delivered' status is evaluated regardless of when delivery occurred — this means you filter the window on created_at only, not delivered_at, which is a subtle but critical distinction that separates strong candidates from average ones.
Clarify the 7-day window by filtering orders where created_at falls within a specific date range (e.g., BETWEEN '2024-01-01' AND '2024-01-07'). Confirm whether the window is fixed historically or rolling, and use inclusive/exclusive bounds consistently.
Within the scoped cohort, flag an order as delivered only when status = 'delivered' AND delivered_at IS NOT NULL. Note that the delivery event can occur outside the 7-day window — what matters is that the order was created within it.
Group the filtered dataset by city and courier_type, computing COUNT(*) for total created orders and SUM of the delivered flag for delivered orders. This gives the numerator and denominator needed for the conversion rate.
Calculate conversion_rate = delivered_count / NULLIF(created_count, 0) to avoid division-by-zero errors. Optionally multiply by 100 and round to a meaningful decimal precision for readability.
Sanity-check the output by verifying that conversion rates fall between 0 and 1 (or 0–100%), and flag any city/courier_type combinations with very low order volumes where rates may be statistically unreliable. Consider adding a volume threshold or confidence note.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The zero-fill requirement is where people mess up.
Start by generating a complete spine of all (month, courier_type) combinations for the last 3 months using a cross join, then left-join actual order data onto that spine so zero-activity months are preserved. Aggregate delivered counts and created counts separately, then compute GMV as delivered_count multiplied by the fixed rate per courier type.
Pro tip: Explicitly call out the 'zero-activity month' requirement as a classic completeness trap — interviewers at quant firms like SIG use this to test whether you default to INNER JOINs (which silently drop missing periods) versus a deliberate spine-based approach with CROSS JOIN and COALESCE.
Generate a reference table of the last 3 calendar months (e.g., using DATE_TRUNC + GENERATE_SERIES or a CTE with explicit month values) to ensure every month appears in the output regardless of activity.
Pull the distinct list of courier types from the reference/dimension table (or from the orders table itself) so you can cross join it with the date spine to create every (month, courier_type) combination.
Left-join the orders table onto the spine, grouping by month and courier type to compute COUNT of orders created and COUNT of orders with a delivered status, using COALESCE to replace NULLs with zero.
Multiply the delivered count by the fixed rate per courier type (either a constant or looked up from a rates table) to derive GMV, ensuring the rate is applied after the COALESCE so zero-activity rows yield GMV of 0.
Order results by month and courier type, then sanity-check that every expected (month, courier_type) row is present and that aggregate totals align with known benchmarks or a simpler rollup query.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Filter out non-delivered orders first, then use a window function to assign row numbers partitioned by user and ordered by time, resetting the sequence whenever a non-biker delivery is encountered. Identify consecutive biker-only streaks of length ≥ 3 and extract the first such streak per user, returning the 1st and 3rd order IDs along with the timestamp difference in minutes.
Pro tip: At SIG, precision in edge-case handling signals strong analytical thinking — explicitly address what happens when a user has multiple streaks of exactly 3 (return only the earliest one) and when non-delivered orders appear mid-streak (they are invisible, so two biker deliveries separated by a non-delivered order still count as consecutive).
Remove all non-delivered orders from the dataset entirely, as they are irrelevant to streak calculation. Assign a row number (e.g., delivered_rank) partitioned by user_id and ordered by order timestamp to establish a clean sequence of delivered orders.
Within the filtered delivered orders, add a boolean flag indicating whether each order was delivered by a biker. Any non-biker delivery resets the streak, so this flag is the key signal for grouping.
Use the classic 'gaps and islands' technique: compute delivered_rank minus a row number partitioned by user_id and biker_flag to create a group identifier. Rows with the same group ID and biker_flag = true form a consecutive biker streak.
Count the size of each streak group and filter to those with at least 3 orders. Within each qualifying streak, use ROW_NUMBER() to label positions 1, 2, 3... so you can pinpoint the 1st and 3rd orders.
Rank the valid streaks per user by the timestamp of their first order and keep only rank = 1 (the earliest streak). Return the order_id of position 1, the order_id of position 3, and DATEDIFF or TIMESTAMPDIFF in minutes between their timestamps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard once you know percentile_cont.
Approach this as a SQL aggregation problem requiring window functions or percentile functions, filtering for delivered orders within the last 30 days and grouping by courier type. Use a database-appropriate percentile function (e.g., PERCENTILE_CONT in standard SQL or APPROX_PERCENTILE in distributed systems) to compute the 95th percentile of delivery duration in minutes. Clearly define delivery time as the difference between delivery timestamp and order creation timestamp.
Pro tip: At a quant-driven firm like SIG, mention awareness of approximate vs. exact percentile functions — APPROX_PERCENTILE is faster on large datasets but trades off precision, which is a real engineering decision worth flagging. Also note that outliers (e.g., cancelled-then-redelivered orders) can skew percentile results and should be discussed.
Identify the relevant table(s) and columns: order creation timestamp, delivery timestamp, order status, and courier type. Confirm that 'delivery time' means the elapsed minutes between order creation and delivery completion.
Apply WHERE conditions to restrict rows to orders with status = 'delivered' and where the order creation timestamp falls within the last 30 days (e.g., created_at >= CURRENT_DATE - INTERVAL '30 days').
Calculate delivery time in minutes using timestamp arithmetic, such as EXTRACT(EPOCH FROM (delivered_at - created_at)) / 60 in PostgreSQL or DATEDIFF('minute', created_at, delivered_at) in other dialects.
Group by courier_type and apply PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY delivery_minutes) for the 95th percentile, along with COUNT(*) for the group size.
Check for NULL delivery timestamps or negative durations that could indicate data quality issues, and briefly interpret what a high 95th percentile for a specific courier type would mean operationally.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by identifying all distinct cities from both the users and couriers tables using a UNION to create a complete city dimension, then cross join it with all courier types and all months to build a complete spine of combinations. Finally, left join the actual order data onto this spine so that missing combinations naturally appear as zero rather than being omitted.
Pro tip: Explicitly calling out that cities must be sourced from BOTH users and couriers tables (not just one) signals you understand real-world data asymmetry — cities may have couriers but no users yet, or vice versa — which is the kind of edge case that separates strong analysts from average ones.
Use UNION (not UNION ALL) on the city column from both the users and couriers tables to get every distinct city that appears in either source, eliminating duplicates.
Cross join the city dimension with all distinct courier types and all relevant months (e.g., from a date dimension or derived from the orders table) to produce every possible combination as the reporting spine.
Separately aggregate the orders table by city, courier type, and month to get real volume counts, keeping this as a subquery or CTE before joining.
Left join the aggregated order data onto the full spine using city, courier type, and month as join keys, then use COALESCE to replace NULL order counts with zero.
Verify the total row count equals cities × courier_types × months, and confirm that the sum of the new report's volume matches the original report's total to ensure no data was lost or duplicated.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Approach this as a filtering and derived-column problem: first scope the dataset to orders at or before the cutoff timestamp, then compute the delivery duration and apply the two-hour threshold logic. Handle NULL delivered_at values explicitly using COALESCE or IS NULL checks, and surface the result as a boolean flag column for downstream use.
Pro tip: At a quant-driven firm like SIG, interviewers appreciate when you proactively mention edge cases such as timezone normalization, clock skew between systems, or whether 'created_at' and 'delivered_at' live in the same timezone — it signals production-level thinking beyond just writing correct SQL.
Confirm the data types of created_at and delivered_at (TIMESTAMP vs. DATETIME) and how the cutoff value is supplied — as a parameter, a variable, or a hardcoded literal. This prevents silent type-mismatch errors.
Use a WHERE clause with created_at <= :cutoff_timestamp to scope the result set before applying any delivery logic, keeping the query efficient and the intent clear.
Calculate the elapsed time between created_at and delivered_at using TIMESTAMPDIFF (MySQL), EXTRACT(EPOCH FROM ...) (PostgreSQL), or DATEDIFF with appropriate units, depending on the SQL dialect in use.
Flag an order as stale when delivered_at IS NULL OR the computed duration exceeds 7200 seconds (2 hours). Use a CASE WHEN expression or a boolean cast to produce the flag column, e.g., CASE WHEN delivered_at IS NULL OR delivery_seconds > 7200 THEN TRUE ELSE FALSE END AS is_stale.
Sanity-check the output by checking counts of stale vs. non-stale orders and verifying a few NULL rows manually. Mention how this flag could feed into a root-cause analysis — e.g., joining with courier or region tables to identify systemic delays.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.