{"id":4072248,"url":"https://alion.io/job/digital-zone-senior-data-engineer","title":"Senior Data Engineer","company":{"id":9375,"name":"Digital Zone","domain":"dz.iq","url":"https://alion.io/company/digital-zone","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Baghdad, Iraq","Dubai, United Arab Emirates"],"countries":["IQ","AE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Aurora","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"Dagster","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"MLFlow","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Feature Store","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-11-11T13:58:19Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-11T13:58:19Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"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.\nWhat you'll do\nDesign, 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.\nModel data for the warehouse and the semantic layer, turning agreed metric definitions into tested, documented models that give the same answer every time.\nTreat 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.\nPartner 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.\nWork 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.\nHandle sensitive data correctly by design: access control, masking and retention for PII (passport and telecom identifiers) across markets with different regulatory expectations.\nWeigh cost against value in warehouse compute, storage and tooling, and make the tradeoffs visible.\nUse 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.\nMentor 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.\nRequirements\nWhat you'll bring\n5 to 8 years building production software or data systems, with 3+ years in data engineering owning pipelines the business depended on.\nExpert SQL and strong Python, solid dimensional modelling, and the judgment to choose the right model for the use case.\nHands-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.\nHands-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).\nExperience with both batch and streaming ingestion, including change data capture, late and duplicate events, and schema evolution.\nA correctness mindset: you reconcile numbers against the source, and you know a pipeline that runs green can still be wrong.\nCommunication that lands with technical and non-technical audiences, including the patience to get stakeholders to agree on what a metric means.\nNice to have\nAnalytics experience on e-commerce, payments, marketplace or fintech products, including multi-currency and multi-market reporting.\nExperience with a feature store or MLOps platform, or with ML use cases such as fraud, risk or personalisation.\nExperience on an early-stage data team, or consolidating fragmented reporting across business units.\nFamiliarity with TypeScript or Go, to work comfortably in the services that produce DZ's data.\nBenefits\nImmediate, large-scale impact on a high-growth business\nTop-of-the-market compensation packages\nWork alongside top regional talent, with team members from Talabat, Careem, Etisalat, and 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