Start by clarifying the schema and definitions (e.g., closed-stage, win condition, sales cycle). Then write a single SQL query that filters deals to Q2 2025, closed stages, and amount > 0, joins with reps, and computes the required metrics using conditional aggregation. Finally, sort the results by win rate descending and average deal amount descending.
Pro tip: Use conditional aggregation (e.g., SUM(CASE WHEN is_win THEN 1 ELSE 0 END)) to compute wins and closed deals in one pass, and be explicit about how you handle NULLs or zero denominators for win rate and average sales cycle.
Confirm table structures, stage values, win condition, and sales cycle calculation. Ensure you understand what 'closed-stage' means and how to identify wins.
Filter deals to Q2 2025 (April 1 - June 30), closed stages, and amount > 0. Join with reps table to get rep information.
Use conditional aggregation to calculate number of closed deals, wins, win rate, average deal amount, and average sales cycle in days. Handle division by zero for win rate.
Sort results by win rate descending, then by average deal amount descending. Ensure all metrics are properly rounded or formatted as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and business definitions, then break the problem into three CTEs: deduplicated touches, first touch per account in 2025, and closed deals. Use a UNION ALL to combine per-segment aggregates with a channel-level overall row, ensuring win rate is computed as wins divided by closed deals.
Pro tip: Explicitly state that you would validate the deduplication logic and handle ties or missing data before writing the final query, and mention that you'd check for accounts with no touches or deals to avoid misleading win rates.
Ask about table structures, definitions of 'rep segment', 'closed deals', and 'wins', and confirm that 'first touch channel' means the channel of the earliest touch in 2025.
Use ROW_NUMBER() OVER (PARTITION BY account_id, touch_date, channel ORDER BY touch_id) to keep the smallest touch_id per group.
From deduplicated touches, filter to 2025 and use ROW_NUMBER() OVER (PARTITION BY account_id ORDER BY touch_date, touch_id) to select the earliest touch per account.
Join first touch to 2025 closed deals on account_id, then group by first_touch_channel and rep_segment to compute closed deals, wins, and win rate.
Use UNION ALL to append a channel-level overall row (grouped only by first_touch_channel) to the per-segment results, ensuring consistent column order and data types.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I stared at this for a solid 30 seconds before writing anything.
Break the problem into four stages: build weekly call counts per rep, build weekly win rates per rep, lag call counts by one week within each rep, and then compute Pearson correlation using the closed-form formula. Ensure you filter to Q2 2025 weeks and exclude reps with fewer than 5 overlapping weeks. Use window functions for lagging and aggregation, and handle edge cases like zero variance.
Pro tip: Always verify that your lagged call count aligns with the correct week (e.g., prior week's calls predicting current week's win rate) and that you're using the same set of weeks for both metrics. Also, consider the business implication: a positive correlation might suggest that increased call activity drives wins, but be cautious about causality.
Aggregate call data to get total calls per rep per week for Q2 2025. Ensure weeks are defined consistently (e.g., using date_trunc).
Compute win rate per rep per week as wins divided by total opportunities (or deals) for that week. Handle division by zero.
Use a window function (e.g., LAG) partitioned by rep and ordered by week to shift call counts by one week, so each row has prior-week calls and current-week win rate.
For each rep, calculate Pearson correlation using the closed-form formula: (n*Σxy - Σx*Σy) / sqrt((n*Σx² - (Σx)²)*(n*Σy² - (Σy)²)). Exclude reps with fewer than 5 overlapping weeks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and table relationships, then write each check as a separate SQL query that joins the relevant tables and applies the condition. For each check, explain the logic, potential edge cases, and how you would validate the results.
Pro tip: Mention that you would add a tolerance for timezone differences or data entry delays, and that you would run these checks regularly as part of a data quality monitoring pipeline.
Identify the tables involved (e.g., deals, accounts) and their key columns (created_at, closed_at, first_touch_date, region). Clarify relationships and join keys.
For deals where closed_at < created_at, write a simple SELECT with a WHERE clause on the deals table. Consider if closed_at can be NULL.
For accounts whose first touch date is after their first deal close date, join accounts to deals, group by account, and compare MIN(first_touch_date) with MIN(closed_at).
For deals whose region does not match their account's region, join deals to accounts on account_id and compare region fields.
Explain how you would validate the checks (e.g., sample records, expected counts) and suggest automating them as part of a data quality dashboard.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the deduplication rule and the fixed reference date. Then, filter calls to the last 7 days, deduplicate accounts per rep, count distinct accounts and total calls, and rank reps by distinct accounts, breaking ties by total calls and then lower rep_id.
Pro tip: Explicitly state your assumptions about the deduplication rule and reference date, and mention how you would handle ties and edge cases like reps with no calls.
Confirm the deduplication rule, the fixed reference date, and the definition of 'last 7 days' (e.g., including or excluding the reference date).
Filter call records to the last 7 days from the reference date. Apply the deduplication rule to ensure each account is counted only once per rep.
For each rep, compute the number of distinct accounts touched and the total number of calls made in the period.
Sort reps by distinct accounts descending, then by total calls descending, then by rep_id ascending. Select the top 3.
Check for ties, missing data, or anomalies. Present the top 3 reps with their metrics and explain the tie-breaking logic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.