Data QA Engineer

About ClearGrid

ClearGrid is revolutionizing the debt resolution industry with cutting-edge AI and advanced analytics. We leverage real-time data machine learning and automation to optimize debt recovery for financial institutions. As we scale our technology platform we are building high-performance resilient systems to support our next phase of hyper-growth.

Why this role exists

ClearGrid runs collections journeys across AI voice human agents SMS WhatsApp and email for lenders in the UAE and KSA. Every decision who we call when we call them what a promise-to-pay is worth what we invoice a client sits on top of data that moves from MongoDB and telephony providers into BigQuery through dbt and out into Tableau dashboards Streamlit apps and client-facing reports. When that data is wrong the consequences are not cosmetic: we call a borrower who has already paid we mis-state a lenders recovery rate in a QBR or we bill against the wrong call window. This role exists to stop that happening and to catch it fast when it does.

What youll own

Test coverage in dbt across staging intermediate and mart layers not just notnull and unique but business-rule tests that encode how collections actually works (DPD bucket transitions PTP date logic disposition hierarchies currency and amount sanity checks).

Source-to-warehouse reconciliation between MongoDB telephony systems client files and BigQuery: row counts per-lender counts per-lender-per-DPD-bucket counts and orphan/drift detection.

Freshness volume and schema-drift monitoring on critical tables with clear ownership of what broken means for each.

Inbound client file QA validating lender account files (fields formats phone numbers names dates encodings) before they ever reach a journey.

Report and dashboard QA every Tableau dashboard and client report gets checked before it ships. Do the headline numbers reconcile to the warehouse Do the filters date ranges and parameters change the figures the way they should Do totals hold when you slice by lender by campaign by DPD bucket Are nulls being silently dropped from a denominator Does the same metric agree across two dashboards that both claim to show it

Pre-release QA of new dbt models and metric definitions before anything downstream is built on them.

The data incident process detection triage stakeholder comms root cause and the fix that stops

the repeat.

What were looking for

Must Haves:

2 years in data quality analytics engineering BI QA or a data analyst role with heavy quality ownership.

Strong SQL CTEs window functions incremental patterns and awareness of query cost. BigQuery/GoogleSQL preferred; any warehouse dialect considered.

Python for validation scripts and automation (pandas or equivalent).

Git-based workflow: branches PRs code review.

An investigative temperament. You are not satisfied by the numbers changed you want to know

which record which join which timezone.

Clear written communication. Much of the job is explaining a data problem to someone who doesnt

want to hear about it.

Nice to have

Collections lending or fintech domain knowledge DPD buckets roll rates PTP recovery rate settlement.

Prefect (or Airflow) orchestration.

MongoDB and semi-structured/JSON data handling.

Streamlit or any BI tool (Tableau Power BI Looker) as a user not necessarily a builder.

Data quality frameworks: dbt-expectations Elementary Great Expectations or similar.