IntermediatePROBLEM_SOLVING
You are given a messy dataset exported from three different systems (spreadsheets, a CRM, and an internal tool) and asked to produce a monthly performance report. Describe, in detail, how you would clean, reconcile, and validate the data before presenting any metrics to leadership.
Custom Role
General

Sample Answer

Problem: I received three inconsistent exports (Excel, Salesforce CRM, and an internal Postgres tool) to build a monthly performance report for ~10K customer accounts. Solution: First, I profiled each dataset in Python (Pandas) and SQL: row counts, null rates, duplicate keys, and date ranges. I standardized keys (account_id, month) and dimensions (UTC dates, ISO country codes) and created a mapping table for mismatched IDs. Using a staging schema in Snowflake, I built transformation views to de-duplicate, join, and aggregate, logging all assumptions. I reconciled totals vs. system-of-record (Salesforce) within ±0.5% for revenue and ±1% for activity counts, investigating any variance above thresholds. Finally, I spot-checked 20 high-value accounts with Sales and created a data-quality summary for leadership. Impact: Delivered a trusted report in 2 days, later automated via dbt and scheduled Power BI dashboards, saving ~6 hours/month.

Keywords

Initial data profiling with Python/SQL to understand quality and coverageStandardize keys and dimensions; use mapping tables for cross-system IDsReconcile against system-of-record with clear variance thresholdsDocument assumptions and provide a data-quality summary with the report
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