About the Role
As a Senior Data Engineer, you will build the pipelines and models that the organization will rely on. Operating across several verticals and markets—digital goods, ticketing, travel, and trading in Iraq, the UAE, and KSA—each sector has developed its own reporting and metrics. As one of the first engineers in the new data team, you will own core parts of the data platform end to end, from ingestion off operational systems and third parties to the modeled tables used by finance, commercial, and product teams for decision-making. This role focuses on ensuring that data is correct, fresh, and trusted by default to eliminate reliance on who pulled it.
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What You'll Do
- Design, build and own production pipelines end to end, both batch and streaming: ingestion from services (Postgres/Aurora, event streams) and third-party providers, incorporating idempotent loads and safe backfills that can be rerun.
- Model data for the warehouse and semantic layer, converting agreed metric definitions into tested, documented models that consistently yield the same answers.
- Treat data quality as an integral part of your role: implement tests, maintain freshness SLAs, address lineage, and set up alerting to catch upstream breaks before stakeholders are affected. You will actively participate in the team's on-call schedule.
- Collaborate with data science teams to transition models from notebook to production: focusing on feature pipelines, training datasets, model deployment and serving, as well as monitoring for drift and data quality post-launch.
- Partner with product and engineering teams on event tracking and data contracts to ensure instrumentation is meticulously designed into services, preventing schema changes from silently breaking pipelines.
- Manage sensitive data appropriately by design, ensuring access control, masking, and retention for PII (such as passport and telecom identifiers) across markets with varied regulatory expectations.
- Evaluate cost versus value in warehouse compute, storage, and tooling decisions, making tradeoffs visible to stakeholders.
- Utilize AI-assisted development judiciously: review generated SQL and pipeline code as peer PR, ensuring its accuracy against real data, not just its operational status.
- Mentor teammates and maintain platform clarity through development of runbooks, Architecture Decision Records (ADRs), and documented models, ensuring that knowledge is distributed rather than siloed.
What You'll Bring
- 5 to 8 years of experience building production software or data systems, with at least 3 years in data engineering owning business-critical pipelines.
- Expertise in SQL and strong proficiency in Python, alongside solid dimensional modeling skills and the ability to select appropriate models for specific use cases.
- Hands-on experience with cloud warehouses and modern orchestration and transformation tools (such as dbt, Airflow, or Dagster), as well as a working knowledge of Postgres and AWS.
- Practical experience in deploying and operating ML models in production, working alongside data scientists on feature pipelines, batch or real-time serving, model versioning, and monitoring (e.g., with MLflow, SageMaker, or similar).
- Familiarity with both batch and streaming ingestion methods, including change data capture, management of late and duplicate events, and schema evolution.
- A correctness mindset: the ability to reconcile numbers against the source and awareness that a pipeline that runs green can still be incorrect.
- Strong communication skills that resonate with both technical and non-technical audiences, including the patience to facilitate stakeholder agreement on metric definitions.
Nice to Have
- Analytics experience in e-commerce, payments, marketplace, or fintech industries, particularly with multi-currency and multi-market reporting.
- Familiarity with a feature store or MLOps platform, or experience with ML use cases such as fraud detection, risk management, or personalization.
- Background in an early-stage data team or experience consolidating fragmented reporting across various business units.
- Knowledge of TypeScript or Go for effective collaboration within services that generate data.
Benefits
- Immediate, large-scale impact on a high-growth business.
- Top-of-the-market compensation packages.
- Collaborate with top regional talent, including team members from leading organizations.
