At a Glance
- Category
- 💻 Technology
- Level
- Senior
- Experience
- 5-8 years
- Type
- Full-time
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As a Senior Data Engineer, you will build the pipelines and models the rest of DZ will run on. DZ operates across several verticals and markets: digital goods, ticketing, travel and trading, in Iraq, the UAE and KSA. Each has grown its own reporting and its own version of the numbers. 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 through to the modeled tables that finance, commercial and product use to make decisions, and the path that takes data science models from notebook to production. This role is about making data correct, fresh and trusted by default, so the answer stops depending on who pulled it.
What you'll do
• Design, build and own production pipelines end to end, batch and streaming: ingestion from DZ's services (Postgres/Aurora, event streams) and third-party providers, with idempotent loads and backfills that are safe to rerun.
• Model data for the warehouse and the semantic layer, turning agreed metric definitions into tested, documented models that give the same answer every time.
• Treat data quality as part of the job, not a cleanup: tests, freshness SLAs, lineage and alerting that catch upstream breaks before a stakeholder does. You'll be an active participant in the team's on-call.
• Partner with data science to take models from notebook to production: feature pipelines, training datasets, model deployment and serving, and monitoring for drift and data quality once they are live.
• Work with product and engineering teams on event tracking and data contracts, so instrumentation is designed into services rather than bolted on, and schema changes stop silently breaking pipelines.
• Handle sensitive data correctly by design: access control, masking and retention for PII (passport and telecom identifiers) across markets with different regulatory expectations.
• Weigh cost against value in warehouse compute, storage and tooling, and make the tradeoffs visible.
• Use AI-assisted development with judgment: review generated SQL and pipeline code like a peer PR, and check it against real data, not only whether it runs.
• Mentor teammates and keep the platform legible through runbooks, ADRs and documented models, so you are not the only person who can support a critical pipeline.
What you'll bring
- 5 to 8 years building production software or data systems, with 3+ years in data engineering owning pipelines the business depended on.
- Expert SQL and strong Python, solid dimensional modelling, and the judgment to choose the right model for the use case.
- Hands-on experience with a cloud warehouse and modern orchestration and transformation tooling (for example dbt, Airflow or Dagster), plus working knowledge of Postgres and AWS.
- Hands-on experience deploying and operating ML models in production alongside data scientists: feature pipelines, batch or real-time serving, model versioning, and monitoring (for example with MLflow, SageMaker or similar).
- Experience with both batch and streaming ingestion, including change data capture, late and duplicate events, and schema evolution.
- A correctness mindset: you reconcile numbers against the source, and you know a pipeline that runs green can still be wrong.
- Communication that lands with technical and non-technical audiences, including the patience to get stakeholders to agree on what a metric means.Nice to have
- Analytics experience on e-commerce, payments, marketplace or fintech products, including multi-currency and multi-market reporting.
- Experience with a feature store or MLOps platform, or with ML use cases such as fraud, risk or personalisation.
- Experience on an early-stage data team, or consolidating fragmented reporting across business units.
- Familiarity with TypeScript or Go, to work comfortably in the services that produce DZ's data.
- Immediate, large-scale impact on a high-growth business
- Top-of-the-market compensation packages
- Work alongside top regional talent, with team members from Talabat, Careem, Etisalat, and more
Requirements
- •5 to 8 years building production software or data systems
- •3+ years in data engineering owning business-critical pipelines
- •Expert SQL and strong Python skills
- •Solid dimensional modelling expertise
- •Hands-on experience with cloud warehouse and modern orchestration (dbt, Airflow, or Dagster)
- •Working knowledge of Postgres and AWS
- •Experience deploying and operating ML models in production (MLflow, SageMaker, etc.)
Responsibilities
- •Design, build, and own production pipelines end-to-end (batch and streaming)
- •Model data for the warehouse and semantic layer with tested, documented models
- •Implement data quality tests, freshness SLAs, lineage, and alerting
- •Participate in team on-call rotations
- •Partner with data science on feature pipelines, training datasets, and model deployment
- •Collaborate with product and engineering on event tracking and data contracts
- •Handle sensitive data (PII) with access control, masking, and retention
- •Mentor teammates and maintain platform legibility through runbooks and ADRs
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- See if your CV gets past the software before Digital Zone sees it
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