Data QA Engineer

About ClearGrid

ClearGrid is transforming the debt resolution sector by applying advanced AI, real-time data, machine learning, and automation to improve debt recovery for financial institutions in the UAE and KSA. We are in a phase of rapid growth, developing robust, scalable systems.

Role Purpose

The Data QA Engineer ensures the integrity and accuracy of data governing collections journeys that span AI voice, human intervention, SMS, WhatsApp, and email channels. Accurate data is critical to avoid errors like contacting borrowers who have already paid or misreporting recovery rates, which directly impact client outcomes and billing.

Key Responsibilities

• Implement comprehensive testing within dbt across staging, intermediate, and mart data layers, covering not only basic tests but also business-rule validations such as DPD bucket transitions, promise-to-pay date logic, disposition hierarchies, and financial sanity checks.
• Reconcile data between source systems like MongoDB, telephony platforms, client files, and BigQuery by monitoring row counts, per-lender and per-DPD-bucket counts, and detecting orphan records and data drift.
• Monitor data freshness, volume, and schema changes on critical tables, defining clear criteria for determining when data is faulty.
• Perform quality checks on incoming client files ensuring fields, formats, phone numbers, names, dates, and encodings meet standards before use in collections journeys.
• Validate reports and dashboards prior to release by confirming that metrics align with warehouse data, filters and parameters function correctly, and all segmentations and totals are accurate and consistent.
• Conduct pre-release quality assurance of new dbt models and metric definitions before downstream usage.
• Manage data incident processes, including detecting issues, triaging, communicating with stakeholders, identifying root causes, and implementing solutions to prevent reoccurrence.

Required Qualifications and Skills

• Minimum 2 years’ experience in data quality, analytics engineering, BI QA, or roles emphasizing ownership of data quality.
• Advanced proficiency in SQL, including CTEs, window functions, incremental logic, and understanding query performance; experience specifically with BigQuery or similar data warehouse dialects.
• Proficiency in Python for developing validation scripts and automating testing, especially using pandas or equivalent libraries.
• Experience working within git-based development workflows including branching, pull requests, and code reviews.
• Strong investigative skills with a desire to trace data discrepancies down to specific records or data joins, not just surface-level differences.
• Excellent written communication to clearly articulate data issues to stakeholders who may not have technical backgrounds.

Preferred but Not Mandatory

• Domain knowledge in collections, lending, or fintech concepts such as DPD buckets, roll rates, promise-to-pay (PTP), recovery rates, and settlements.
• Familiarity with workflow orchestration tools like Prefect or Airflow.
• Experience handling semi-structured data formats, particularly MongoDB and JSON.
• Usage exposure to BI tools like Streamlit, Tableau, Power BI, or Looker from a user perspective.
• Knowledge of data quality frameworks such as dbt-expectations, Elementary, or Great Expectations.