DoorDash Key Metrics & Dashboard Requirements Questions

Defining and standardizing DoorDash KPIs, identifying data sources, calculating metric definitions, data governance, and designing dashboards and reporting pipelines to monitor product and business performance. Includes data visualization best practices, dashboard design, interactivity, drill-down capabilities, and alignment with business goals across operations, product, and marketplace analytics.

EasyTechnical

89 practiced

A product manager wants a metric 'average time-to-pickup' for dashers on DoorDash. Define this metric rigorously, list potential sources of bias or data quality issues, and propose corrections or guardrails to ensure reliable measurement.

EasySystem Design

123 practiced

Design a dashboard layout for an executive-level DoorDash operations dashboard that must surface top-line health and five-minute drill signals for immediate issues. Describe the layout, key visualizations, and interactivity you would include.

MediumTechnical

74 practiced

Propose a QA test suite for critical metric computations in your BI layer. Include unit tests, integration tests, and data-acceptance tests with example assertions for DoorDash metrics.

HardTechnical

81 practiced

A dashboard shows a metric with high variance day-to-day. Propose 3 statistical smoothing or aggregation strategies to present a more stable trend without hiding real issues, and explain when each is appropriate.

MediumTechnical

90 practiced

Write a Python (pandas) function to compute cohort retention: percentage of consumers in a weekly acquisition cohort who place at least one order in each subsequent week up to 12 weeks. Describe input dataframe schema and edge-case handling.

Sample Answer

I define input df with columns: user_id (int), order_id, order_ts (datetime UTC), acquisition_date (date of first order). Function returns DataFrame with weeks 0..12 retention %. Implementation (pandas):

python

import pandas as pd

def weekly_cohort_retention(df):
    # df: user_id, order_ts (datetime), acquisition_date (date)
    df = df.copy()
    df['acq_week'] = pd.to_datetime(df['acquisition_date']).dt.to_period('W').apply(lambda r: r.start_time)
    df['order_week'] = pd.to_datetime(df['order_ts']).dt.to_period('W').apply(lambda r: r.start_time)
    df['week_offset'] = ((df['order_week'] - df['acq_week']).dt.days // 7).clip(lower=0)
    cohort = df.groupby(['acq_week','user_id'])['week_offset'].min().reset_index()
    # build pivot: unique users per cohort and week
    pivot = cohort.pivot_table(index='acq_week', columns='week_offset', values='user_id', aggfunc='nunique').fillna(0)
    cohort_sizes = pivot.iloc[:,0]
    retention = pivot.div(cohort_sizes, axis=0).iloc[:,:13] * 100
    return retention.round(2)

Edge cases: users with timezone skew handled by normalizing to UTC; users with multiple orders in week counted once; cohorts with <10 users flagged; handle missing acquisition_date by using min(order_ts).

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