This is where I spent most of my energy and still felt like I was only halfway there.
Start by clarifying the dashboard's requirements—what KPIs, refresh cadence, and query patterns—then propose a daily aggregated fact table at the grain of (date, dimension keys) with precomputed metrics. Explain how you'd partition by date, cluster by common filters, and incrementally load only new or changed data to keep the dashboard fast and cost-efficient.
Pro tip: Mention that you'd validate the design by writing example queries and checking that they hit the precomputed aggregates without scanning raw data—this shows you think about real-world performance, not just schema aesthetics.
Ask about the KPIs, update frequency, and typical query filters (e.g., date range, user segment). Define the grain as one row per day per relevant dimension combination (e.g., date, country, platform).
Choose a composite primary key of date + dimension IDs to ensure uniqueness. Include precomputed metrics like daily active users, photos uploaded, and engagement rates, and decide which dimensions to include based on query patterns.
Precompute heavy aggregations (e.g., distinct counts, sums) that are expensive over raw data. Leave flexible metrics (e.g., ratios, percentiles) for query time if they can be derived from precomputed components or if the dashboard allows approximate results.
Partition by date to enable partition pruning and easy backfills. Cluster by high-cardinality dimensions used in filters (e.g., user_id, country). For incremental loads, process only new partitions and handle late-arriving data with a lookback window or merge logic.
Discuss trade-offs: precomputing more reduces query latency but increases storage and ETL complexity. Validate by running representative queries and checking they use the aggregates efficiently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope and assumptions (e.g., batch vs. streaming, data volume, freshness requirements) before diving into the pipeline. Then walk through the ETL stages—extract, transform, load—focusing on how raw source tables are incrementally processed into KPI tables, and highlight trade-offs like full refresh vs. incremental, idempotency, and data quality checks.
Pro tip: Emphasize incremental processing and idempotency—interviewers at Nextdoor care about scalability and reliability. Mention how you'd handle late-arriving data and backfills without disrupting daily runs.
Ask about data volume, update frequency, SLA for KPI freshness, and whether the pipeline is batch or streaming. State your assumptions clearly to frame the rest of the answer.
Describe how you'd pull data from raw source tables—e.g., using incremental extracts based on a high-watermark (last updated timestamp) or change data capture (CDC). Mention handling late-arriving data and deduplication.
Outline the transformation logic: joins, filters, aggregations, and business logic to compute KPIs. Use SQL or pseudocode to illustrate key steps, and discuss how you'd handle slowly changing dimensions or complex metrics.
Explain how you'd load the transformed data into KPI tables—e.g., using upserts (MERGE) or partition overwrites. Emphasize idempotency and how you'd handle failures and retries.
Describe the orchestration (e.g., Airflow DAG) with dependencies, scheduling, and alerting. Include data quality checks (e.g., row counts, null checks) and how you'd backfill or reprocess data when needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Retention questions always feel cleaner than they are in practice.
Clarify the definition of 'active' and 'exactly one week later' (e.g., day 7 after signup), then outline the data model changes needed to support cohort-based retention analysis. Describe an efficient computation method, such as using a self-join or window functions, and discuss how to integrate it into the dashboard.
Pro tip: Mention that retention is typically calculated on a daily basis, but for week-1 retention, you need to align cohorts by signup date and check activity on the exact day 7. Also, consider using a pre-aggregated table to avoid expensive joins on large datasets.
Clarify what 'active' means (e.g., any event, specific action) and confirm that 'exactly one week later' means day 7 after signup. Determine the granularity (daily cohorts) and the time window for analysis.
Review existing tables for user signups and activity events. Identify if you have a signup date per user and an activity log with timestamps. Determine if a new table or view is needed to efficiently compute retention.
Propose adding a signup cohort date column to the user table if not present, or create a new table that maps user_id to signup_date. For activity, ensure events are partitioned by date for efficient querying. Consider a materialized view or summary table for retention metrics.
Use a SQL query that joins signups with activity on user_id and date difference = 7 days. Alternatively, use window functions to identify active users on day 7. For large datasets, pre-aggregate daily active users per cohort to avoid repeated joins.
Add the retention metric to the dashboard, ensuring it updates regularly. Discuss how to handle time zones, late-arriving data, and backfilling. Consider visualizing retention as a line chart over cohort dates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tripped up on the non-additive metrics immediately.
Start by clarifying the requirements: which metrics are additive vs. non-additive, how partial weeks should be handled, and what 'change vs 7 days ago' means at a weekly grain. Then propose a table design that stores both daily and weekly aggregates, with careful handling of non-additive metrics and partial weeks. Finally, explain the computation logic for weekly rollups and the translation of the comparison metric.
Pro tip: Always discuss the trade-offs between pre-aggregating weekly data for performance and maintaining daily data for flexibility, and suggest a hybrid approach that balances both. Also, consider timezone and week-start conventions, as they can significantly impact weekly rollups.
Ask questions to understand which metrics are additive vs. non-additive, how partial weeks should be treated (e.g., exclude or include with caveats), and what 'change vs 7 days ago' means at a weekly grain (e.g., week-over-week change).
Propose a schema that includes a weekly aggregate table with dimensions like week_start_date, and columns for additive metrics (sums) and non-additive metrics (e.g., distinct counts, averages, ratios) stored as pre-computed values or with sufficient granularity to recompute.
Explain that non-additive metrics like distinct users or averages cannot be summed from daily values; instead, they require either storing daily granularity for recomputation or using approximate algorithms (e.g., HyperLogLog for distinct counts) and storing intermediate states.
Discuss strategies for partial weeks: either exclude incomplete weeks from weekly rollups, include them with a flag, or compute them on-the-fly from daily data. Highlight the impact on trend analysis and comparisons.
Interpret 'change vs 7 days ago' as week-over-week change (current week vs. previous week). Explain that this requires comparing the current week's aggregate to the previous week's aggregate, and discuss how to handle partial weeks in this comparison.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Storing everything in UTC in the raw tables is the obvious starting point and I led with that.
Start by clarifying that timestamps should be stored in UTC and timezone conversion should happen at query/reporting time, not during ingestion. Then walk through each area—timestamp handling, ETL windows, backfills, and dual-timezone support—focusing on how to avoid double-counting at day boundaries by using half-open intervals and event-level deduplication. Emphasize that supporting both timezones requires parameterizing the reporting layer rather than duplicating data.
Pro tip: Propose a 'timezone-aware reporting layer' where the timezone is a parameter, and demonstrate how to handle DST transitions and partial-day backfills without reprocessing entire datasets. This shows you understand both the technical and operational trade-offs.
Ask whether the New York office needs both timezones simultaneously or just a switch, and whether historical data must be restated. Confirm that events are timestamped in UTC or can be converted.
Ensure all timestamps are stored in UTC with timezone metadata. Perform timezone conversion at query time using a reporting parameter, not during ETL, to avoid data duplication.
Redefine ETL windows to align with the target timezone's day boundaries (e.g., midnight ET). For backfills, reprocess only affected partitions using half-open intervals [start, end) to prevent double-counting.
Implement a parameterized reporting layer that accepts a timezone argument. Use event-level deduplication (e.g., by event ID) and ensure day boundaries are handled with exclusive end times.
Compare metrics across timezones for consistency, and set up alerts for anomalies at day boundaries or during DST transitions. Document the approach for future timezone additions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.