Senior Data Engineer

Digital Zone

. Design, build and own production pipelines end to end, including batch and streaming ingestion from DZ services and third-party providers .

Posted 11/12/2026full-timeBaghdad • IraqSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and owning production data pipelines, with a strong focus on SQL and Python. Proficient in data modeling, quality assurance, and collaboration with cross-functional teams to ensure effective data management and analytics.

Highest-signal resume keywords

Expert SQLStrong PythonCloud Warehouse ExperienceData Engineering Pipeline OwnershipBatch And Streaming Ingestion

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Dimensional ModellingData Quality TestsFeature PipelinesModel VersioningChange Data CaptureSchema EvolutionAccess ControlData LineageIdempotent LoadsRerunnable Backfills

Soft Skills

Communication With Technical And Non-Technical AudiencesMentoring Teammates

Tools & Technologies

DbtAirflowDagsterPostgresAWS

Industry Keywords

E-CommercePaymentsMarketplaceFintechMulti-Currency ReportingMulti-Market ReportingMLOpsFraud DetectionRisk ManagementPersonalisation

Tech Stack

Tools & technologies

AirflowAWSCloudPostgresPythonSQLTypeScriptGo

About the role

Key responsibilities & impact

Design, build and own production pipelines end to end, including batch and streaming ingestion from DZ services and third-party providers
Implement idempotent loads and safely rerunnable backfills
Model data for the warehouse and semantic layer using tested and documented metric definitions
Implement data quality tests, freshness SLAs, lineage and alerting
Participate in the team's on-call
Partner with data science to move models from notebook to production, including feature pipelines, training datasets, deployment, serving and monitoring
Collaborate with product and engineering teams on event tracking and data contracts
Implement access control, masking and retention for sensitive PII across markets
Balance warehouse compute, storage and tooling costs against value
Review AI-generated SQL and pipeline code against real data
Mentor teammates and maintain runbooks, ADRs and documented models

Requirements

What you’ll need

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
Hands-on experience with a cloud warehouse and modern orchestration and transformation tooling, such as dbt, Airflow or Dagster
Working knowledge of Postgres and AWS
Hands-on experience deploying and operating ML models in production alongside data scientists, including feature pipelines, batch or real-time serving, model versioning, and monitoring
Experience with both batch and streaming ingestion, including change data capture, late and duplicate events, and schema evolution
Ability to reconcile numbers against the source and identify incorrect pipelines
Communication with technical and non-technical audiences
Nice to have: analytics experience on e-commerce, payments, marketplace or fintech products, including multi-currency and multi-market reporting
Nice to have: experience with a feature store or MLOps platform, or ML use cases such as fraud, risk or personalisation
Nice to have: experience on an early-stage data team or consolidating fragmented reporting across business units
Nice to have: familiarity with TypeScript or Go

Benefits

Comp & perks

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