Senior Data Engineer

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.