Data & Analytics Engineer

Role Purpose

Build and own the data infrastructure that powers Thawani Pay’s growth and decision-making under the Marketing / Growth function. This includes data pipelines, data lake, and data warehouse, ensuring clean, reliable, and timely data across all user and merchant touchpoints, products, and channels.

Key Responsibilities

Data Infrastructure & Pipelines

• Design, implement, and maintain ETL/ELT pipelines from:
• App analytics (GA4, Firebase, Adjust, product / UI/UX analytics tools (e.g., Mixpanel & etc)
• Backend transactional systems (wallet, cards, international transfers, microfinancing, Sama platform, Athar donations)
• Merchant solutions (Tajer, Merchant Portal, Payment Gateway, Payment Link)
• CRM (Microsoft Dynamics 365) and support systems.
• Own the data lake and data warehouse (e.g., BigQuery or equivalent), ensuring it supports analytics, BI (Power BI), and regulatory reporting.

Data Model, Taxonomy & Quality

• Define and maintain a consistent event taxonomy and schema across all platforms (apps, web, backend, CRM).
• Implement robust data quality checks, monitoring, and alerting to detect and resolve data gaps, latency, or anomalies.
• Document data flows, tables, and metric definitions to enable self-serve analytics across Marketing, Product, and Leadership.

Enablement & Integrations

• Enable performant data access for:
• Power BI dashboards (for CMO, CEO, CBO, Board)
• Product & UX analytics (Mixpanel / Hotjar / Clarity)
• CRM and marketing automation (Dynamics 365)
• Risk/Fraud, Finance, and regulatory teams where required.
• Support integration of new partners (card schemes, remittance partners, microfinancing and insurance partners) into the data ecosystem.

Qualifications & Experience

• 7–10+ years of experience in Data Engineering, ideally in FinTech, digital banking, payments, or large-scale digital products.
• Strong proficiency in SQL, Python and modern ETL/ELT orchestration (e.g., Airflow, dbt, or similar).
• Hands-on experience with cloud data warehouses / data lakes (e.g., BigQuery, Snowflake, or similar) and connecting them to BI tools like Power BI.
• Demonstrated experience integrating app analytics, transactional systems, and CRM into a unified analytics stack.
• Ability to work closely with non-technical stakeholders and translate business needs into data solutions.