Snowflake Data Accuracy Validation

I need a Snowflake-savvy analyst to put a rock-solid data-validation layer in place. The immediate task is to design and implement validation routines that run inside our Snowflake environment, check incoming tables against business rules, and surface any accuracy issues before the data is consumed downstream.

Here’s the flow I have in mind: raw data lands in Snowflake, your scripted tests execute, exceptions are logged to an audit table, and a concise accuracy dashboard or CSV summary is generated so the business team can act quickly. Everything must be fully documented so future analysts can extend or modify the checks without hunting for logic buried in code.

Key points
• Scope: data validation only—no transformation or reporting build-out at this stage.
• Goal: ensure data accuracy by catching duplicates, out-of-range values, missing keys, and any other integrity breaks we define together.
• Stack: native Snowflake SQL (tasks, streams, procedures), with optional use of Snowflake’s metadata views; keep external tooling minimal.
• Deliverables:
1. Set of parameter-driven validation scripts/procedures deployed in Snowflake.
2. Audit/error table schema plus sample populated data.
3. Automated run-book or Snowflake Task schedule.
4. Brief walkthrough document and change-log.

If you’ve built similar rule-based checks inside Snowflake and can move quickly, let’s get started right away.