Identify Trends in Lakehouse Engagement | Dataford Interview Questions

Business Context

Databricks wants a clearer view of how users engage with the platform across workspace activity, SQL usage, notebook collaboration, and trial-to-paid conversion. The VP of Marketing asks you to use product interaction data to identify the most important trends from the last two quarters and explain which ones matter for pipeline growth and customer expansion.

Metric Scenario

You are given six months of event-level data for 420,000 registered users across free trials, self-serve accounts, and enterprise workspaces. In the latest quarter, weekly active users grew from 118,000 to 131,000 (+11%), but trial-to-paid conversion fell from 8.4% to 6.9%, 8-week retention for new trial users dropped from 31% to 24%, and average notebooks run per active workspace declined from 14.2 to 12.6. At the same time, Databricks SQL query volume increased 18%, and the share of users interacting with Delta Live Tables rose from 9% to 15%.

Stakeholders want to know whether overall engagement is actually improving, which user segments are driving the changes, and where marketing should focus acquisition and lifecycle programs.

Requirements

Define the core KPI set you would use to identify key trends in this interaction dataset.
Explain how you would distinguish growth in top-line activity from deterioration in conversion or retention quality.
Decompose the changes to isolate the biggest drivers by user segment, acquisition source, workspace type, and product surface.
Identify 3-5 likely hypotheses for the observed metric movements and how you would validate them.
Recommend the top actions Marketing should take based on the trends you find.

Data Available

user_events: event_timestamp, user_id, account_id, workspace_id, event_name, product_surface, session_id, device_type
accounts: account_id, account_type, industry, region, contract_start_date, plan_tier
trial_funnel: user_id, acquisition_channel, trial_start_date, activation_date, paid_conversion_date
workspace_usage_daily: workspace_id, date, notebooks_run, sql_queries, jobs_triggered, dlt_pipeline_runs, active_users
user_profile: user_id, persona, company_size, country, signup_date

Problem

Business Context

Metric Scenario

Stakeholders want to know whether overall engagement is actually improving, which user segments are driving the changes, and where marketing should focus acquisition and lifecycle programs.

Requirements

Define the core KPI set you would use to identify key trends in this interaction dataset.
Explain how you would distinguish growth in top-line activity from deterioration in conversion or retention quality.
Decompose the changes to isolate the biggest drivers by user segment, acquisition source, workspace type, and product surface.
Identify 3-5 likely hypotheses for the observed metric movements and how you would validate them.
Recommend the top actions Marketing should take based on the trends you find.

Data Available

user_events: event_timestamp, user_id, account_id, workspace_id, event_name, product_surface, session_id, device_type
accounts: account_id, account_type, industry, region, contract_start_date, plan_tier
trial_funnel: user_id, acquisition_channel, trial_start_date, activation_date, paid_conversion_date
workspace_usage_daily: workspace_id, date, notebooks_run, sql_queries, jobs_triggered, dlt_pipeline_runs, active_users
user_profile: user_id, persona, company_size, country, signup_date

Problem

Business Context

Metric Scenario

Stakeholders want to know whether overall engagement is actually improving, which user segments are driving the changes, and where marketing should focus acquisition and lifecycle programs.

Requirements

Define the core KPI set you would use to identify key trends in this interaction dataset.
Explain how you would distinguish growth in top-line activity from deterioration in conversion or retention quality.
Decompose the changes to isolate the biggest drivers by user segment, acquisition source, workspace type, and product surface.
Identify 3-5 likely hypotheses for the observed metric movements and how you would validate them.
Recommend the top actions Marketing should take based on the trends you find.

Data Available

user_events: event_timestamp, user_id, account_id, workspace_id, event_name, product_surface, session_id, device_type
accounts: account_id, account_type, industry, region, contract_start_date, plan_tier
trial_funnel: user_id, acquisition_channel, trial_start_date, activation_date, paid_conversion_date
workspace_usage_daily: workspace_id, date, notebooks_run, sql_queries, jobs_triggered, dlt_pipeline_runs, active_users
user_profile: user_id, persona, company_size, country, signup_date

Problem

Business Context

Metric Scenario

Stakeholders want to know whether overall engagement is actually improving, which user segments are driving the changes, and where marketing should focus acquisition and lifecycle programs.

Requirements

Define the core KPI set you would use to identify key trends in this interaction dataset.
Explain how you would distinguish growth in top-line activity from deterioration in conversion or retention quality.
Decompose the changes to isolate the biggest drivers by user segment, acquisition source, workspace type, and product surface.
Identify 3-5 likely hypotheses for the observed metric movements and how you would validate them.
Recommend the top actions Marketing should take based on the trends you find.

Data Available

user_events: event_timestamp, user_id, account_id, workspace_id, event_name, product_surface, session_id, device_type
accounts: account_id, account_type, industry, region, contract_start_date, plan_tier
trial_funnel: user_id, acquisition_channel, trial_start_date, activation_date, paid_conversion_date
workspace_usage_daily: workspace_id, date, notebooks_run, sql_queries, jobs_triggered, dlt_pipeline_runs, active_users
user_profile: user_id, persona, company_size, country, signup_date