Senior Data Engineer

About us:

At Gathern, we’re not just a platform, we’re the homegrown Saudi success story that built and leads the alternative hospitality sector across the Kingdom. As the largest peer-to-peer vacation rental marketplace in Saudi Arabia, Gathern enables travelers to explore the country through authentic stays hosted by local residents  while directly supporting Saudi Vision 2030 by boosting tourism, empowering communities, and expanding accommodation supply.
Backed by our SAR 270 million Series B funding round  led by Sanabil Investments (PIF-owned) and valuing Gathern at over SAR 1 billion! We’re entering an exciting new chapter of growth, innovation, and regional expansion as we prepare for a future Tadawul listing .
With 5M+ users , guests from 150+ nationalities , a network of 72,000+ hospitality units , and more than SAR 2 billion paid to 33,000+ Saudi hosts, Gathern stands as one of the fastest-growing tech companies in the Kingdom, holding a 44% national market share and 53% in Riyadh!
This is your opportunity to join a company that’s redefining travel and shaping the future of tourism in Saudi Arabia!

We are looking for an Engineering Manager who combines strong technical expertise with excellent leadership and operational discipline. You will lead engineering teams, ensure successful delivery of complex projects, optimize processes, elevate performance, and drive technical excellence across the organization. You will play a critical role in shaping engineering culture, scaling systems, improving execution, and aligning technical initiatives with business strategy.

Key Responsibilities:

1. Data Engineering & Pipeline Ownership

• Design, build, and maintain scalable data pipelines feeding the central data warehouse (BigQuery).
• Ensure data accuracy, freshness, reliability, and consistency across all BI and analytics datasets.
• Own data modeling, schema design, and documentation standards.
• Support efficient onboarding of new data sources with minimal rework.
• Optimize data access patterns and performance for BI tools (e.g., Power BI).

2. Data Quality, Observability & Reliability

• Implement and maintain data quality checks and monitoring mechanisms.
• Define and enforce core data quality dimensions (accuracy, completeness, consistency, timeliness, validity).
• Build automated validation checks (freshness SLAs, null thresholds, schema drift detection, referential integrity).
• Monitor data quality metrics and proactively alert on issues before they impact decision-making.
• Troubleshoot, debug, and resolve data pipeline and data quality incidents.

3. Collaboration & Enablement

• Partner closely with BI, Analytics, Product, and Engineering teams to support reporting and insight needs.
• Enable self-service analytics through well-designed, documented, and trusted datasets.
• Clearly document technical decisions, data models, and pipeline logic.
• Support data access controls, permissions, and security best practices in cloud environments.