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.
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 lender's 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.
• Test coverage in dbt across staging, intermediate and mart layers — not just not_null 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.
• 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 doesn't
want to hear about it.
• 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.
Data QA Engineer • Dubai, Dubai, United Arab Emirates
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