Prepare for your next interview.

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1. Coding questions · Disney

   Write complex SQL for streaming funnels

   This question evaluates a candidate's ability to write complex ANSI-SQL for event-level funnel analysis, including temporal windowing, user attribution, multi-stage aggregation, and handling of event schemas.

2. Coding questions · DoorDash

   Write complex SQL on DoorDash data

   This question evaluates proficiency in complex SQL querying and data manipulation, including joins across tables, time-based filtering, window functions, null and edge-case handling, aggregations, and integrating promotion and order-event data.

3. Coding questions · PayPal

   Write conditional aggregation SQL queries

   This question evaluates proficiency with conditional aggregation in SQL, including the use of SUM(CASE WHEN ...) versus filtering with WHERE and the implications of placing aggregation filters in HAVING for grouped queries, within the Data Manipulation (SQL/Python) domain.

4. Coding questions · Block (Square)

   Write left-join queries with tricky filters

   This question evaluates SQL data-manipulation skills including LEFT JOIN semantics, aggregation functions like COUNT(DISTINCT) and SUM, GROUP BY/HAVING logic, and date-window filtering for referral and order analytics.

5. Coding questions · Thumbtack

   Write monthly new-vs-returning requests SQL

   This question evaluates a candidate's ability to perform time-based SQL aggregation and classification, covering window functions to compute each user's first request month, conditional counts for NEW vs RETURNING requests, percentage formatting, and correct handling of UTC timestamps and edge cases.

6. Coding questions · Upstart

   Write monthly touches and last-touch SQL

   This question evaluates proficiency in SQL-based data manipulation and time-based attribution, covering aggregations, joins, window functions, date truncation to calendar months, tie-breaking logic, and handling edge cases like post-conversion touches and duplicate timestamps.

7. Coding questions · Capital One

   Write one SQL for exam scores aggregation

   This question evaluates proficiency in SQL data manipulation and reporting, specifically joins, GROUP BY and aggregate functions, NULL handling (COALESCE/ISNULL), conditional aggregation and set operations to produce combined result sets.

8. Coding questions · Bloomberg

   Write Postgres string parsing and aggregation query

   This question evaluates proficiency in SQL string parsing and regular expressions, type casting to timestamps, date-based filtering, distinct aggregation, and awareness of cross-dialect function equivalence, and is categorized under Data Manipulation (SQL/Python) and database querying.

9. Coding questions · Bloomberg

   Write PostgreSQL string-manipulation query

   This question evaluates proficiency in PostgreSQL string manipulation, regular expressions, URL parsing, key-value parsing and timestamp-based filtering for data transformation tasks.

10. Coding questions · Circle

    Write PostgreSQL updates with date filters

    This question evaluates competency in PostgreSQL data manipulation, covering updates with correct timestamp handling, upserts with conflict resolution, date-range selection using timestamptz, and index design plus performance analysis with EXPLAIN ANALYZE.

11. Coding questions · Apple

    Write queries to compute salary and budget stats

    This set of tasks evaluates data manipulation and analytical competencies, specifically the ability to compute order-statistics on numeric columns, perform relational joins to enrich event records, and reason about constrained selection for maximizing hires using SQL and pandas.

12. Coding questions · Twitch

    Write robust SQL for streaming analytics

    This question evaluates a candidate's ability to author robust SQL for streaming analytics, covering de-duplication, event-time handling, sessionization, concurrency, overlap detection, and retention calculations on time-series view event data.

13. Coding questions · Chime

    Write rolling-window SQL over weekly cohorts

    This question evaluates proficiency with SQL window functions and CTEs for time-based cohort aggregation, including rolling ROWS-based sums, LAG-based week-over-week percent change, DENSE_RANK ranking, and handling first- and second-purchase calculations.

14. Coding questions · Pinterest

    Write SQL and pandas for shopping events

    This question evaluates a candidate's ability to perform data manipulation in SQL and pandas, covering event deduplication, time-window aggregations, next-day retention calculations, top-N ranking, joins, and data cleaning/mapping for analytics.

15. Coding questions · Uber

    Write SQL and Pandas for Uber Trips

    This question evaluates SQL and Pandas data manipulation competencies, including joins, time-based filtering, handling NULLs, aggregations, rate and ratio calculations, and windowed comparisons over grouped city-day data.

16. Coding questions · Meta

    Write SQL and Python for data prep

    This question evaluates data engineering competencies including SQL analytics with window functions and MERGE/INSERT patterns for idempotent incremental jobs, event-time handling (duplicates and late events), sessionization and retention metrics, plus Python-based JSON parsing, normalization, partitioned Parquet output and basic unit testing.

17. Coding questions · Coinbase

    Write SQL and Python for funnels/retention

    This question evaluates SQL and Python data-manipulation skills, including event-to-session joins, temporal window filtering, deduplication, JSON metadata handling, and computation of funnel and retention metrics like daily unique users and conversion rates.

18. Coding questions · Robinhood

    Write SQL and Python for transaction analytics

    This question evaluates data manipulation competency in SQL and Python, covering normalization of transaction direction, JOINs and CASE logic, window functions for ranking and aggregation, and designing single-pass counting algorithms for streaming-like inputs.

19. Coding questions · Meta

    Write SQL filtering, grouping, CASE, UNION tasks

    This question evaluates proficiency with SQL filtering, aggregation, conditional logic, and set operations—specifically skills around WHERE filtering, GROUP BY/HAVING, CASE bucketing, FLOOR on aggregates, and UNION versus UNION ALL—within the Data Manipulation (SQL/Python) domain and emphasizes practical application of ANSI SQL.

20. Coding questions · Coinbase

    Write SQL for 30-day activity and D7 retention

    This question evaluates a candidate's skill in SQL-based data manipulation and product-analytics competencies—specifically aggregation, deduplication to avoid double-counting, date arithmetic for time-windowed queries, cohort retention calculation, and percentage metrics—in the Data Manipulation (SQL/Python) domain.

21. Coding questions · TikTok

    Write SQL for 7-day geo-localized revenue dashboard

    This question evaluates SQL skills in time-zone aware local-date aggregation, idempotent event deduplication, cohorting for new versus returning users, calculation of revenue and conversion metrics, and anomaly detection via median-based drop flags.

22. Coding questions · Meta

    Write SQL for 7-day WhatsApp call metrics

    This question evaluates SQL-based data manipulation and aggregation skills, specifically time-based grouping, filtering by user attributes, deduplication, handling of edge-case durations, and computation of rates and medians from transactional call and event tables.

23. Coding questions · Uber

    Write SQL for active counts and YTD top driver

    This question evaluates a data scientist's SQL competency in aggregation, joins, time zone–aware datetime conversion, filtering on trip status, handling negative fares and zero-value drivers, tie-breaking, and the comparative use of window functions versus GROUP BY for ranking.

24. Coding questions · Meta

    Write SQL for active follow connections

    This question evaluates proficiency in temporal event modeling, time-aware SQL aggregation, and reasoning about state transitions from ordered event streams, testing competencies in building point-in-time and historical views within the Data Manipulation (SQL/Python) domain for data engineering roles, with a primary level of practical application augmented by temporal-conceptual understanding. It is commonly asked to assess the ability to infer current and daily historical relationships from event histories, manage edge cases like multiple request/accept cycles and rejections, and produce efficient queries for current counts and daily snapshots.