Data reconciliation is a common finance analytics task when values in source systems do not align. In a Tenneco-style reporting environment, you may need to compare ERP extracts, subledger feeds, or BlackLine-prepared reconciliation outputs and explain why records differ.
You are given a dataset with discrepancies and are asked how you would reconcile it using SQL. Explain how you would structure your approach to identify missing records, duplicate records, key mismatches, timing differences, and amount variances. Describe what tables you would compare, how you would normalize fields before matching, and how you would classify exceptions so the output is useful for finance review.
The interviewer expects a practical SQL-oriented explanation rather than a generic process answer. Focus on how you would use joins, CTEs, aggregations, and CASE WHEN logic in PostgreSQL to isolate and summarize discrepancies, and how you would make the result auditable.