About the job
Syarah is building the future of automotive e-commerce in the region. The team is looking for a Data Lead in Amman to shape and manage a data ecosystem that supports engineering, analytics, data science, and AI across the business.
Role Overview
This is a hands-on leadership role. The Data Lead will architect scalable data systems, guide a skilled cross-functional team, and act as a key analytical voice for the company. The position involves uncovering insights, identifying risks, and finding opportunities that drive growth in Syarah’s e-commerce and payment platforms.
The Data Lead will oversee Syarah’s end-to-end data platform on Google Cloud Platform (GCP), integrating data from product, payments, ERP, and operations. The goal: ensure business decisions rely on accurate, real-time, and reliable data.
Additional responsibilities include developing AI-driven internal tools, leading data DevOps practices, and translating complex data into actionable recommendations for leadership.
What You Will Do
- Lead and mentor a cross-functional data team, including engineering, analytics, and data science.
- Design and manage the company’s data platform on GCP (using BigQuery, Cloud Run, and Dataform).
- Build scalable ETL pipelines and maintain star schema data models.
- Integrate ERPNext and other operational systems into a unified data architecture.
- Develop and deploy containerized services with Docker on Cloud Run.
- Create and maintain Flask REST APIs for secure data distribution.
- Oversee data DevOps, including CI/CD pipelines and infrastructure management.
- Coordinate workflows using Apache Airflow.
- Integrate and manage Adjust (mobile attribution) and Mixpanel (product analytics) within a unified data lake.
- Ensure accurate event tracking, identity resolution, and funnel analytics across platforms.
- Establish unified product, payments, and financial data models to support revenue reconciliation and performance tracking.
- Proactively identify trends, anomalies, and risks to deliver insights before requests arise.
- Translate data into executive-level business recommendations.
- Define KPI frameworks and build reliable data products for finance, operations, and product teams.
- Maintain data accuracy, reliability, and availability through monitoring, alerts, and adherence to SLA standards.
- Develop internal AI tools using large language models (LLMs) and prompt engineering to improve productivity and automation.
