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Open nowPosted 6 hours ago

Data Engineer

Arabic.AI

Where
Riyadh, Riyadh Province, Saudi Arabia
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Your applicationOpen nowData EngineerArabic.AI · Riyadh, Riyadh Province, Saudi Arabia

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Arabic.AI's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

13.7% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.3%1 day
  2. 4.8%3 days
  3. 13.7%7 days
  4. 22.1%14 days
  5. 37.6%30 days
  6. 59.1%60 days
This job: posted 6 hours ago

The posting

You will build and look after the pipelines that move data from the client's operational systems into the warehouse, and the data models that everything downstream depends on. Ingestion, processing and modelling, plus the documentation that goes with them. You will also do the analyst work that keeps the numbers honest.

What you will do

  • Profile new sources before you build: databases, APIs, files, open data, structured and unstructured.
  • Build, schedule and monitor ingestion and processing pipelines in the tools the client already uses, and extend them where it makes sense.
  • Design and build the warehouse models (star schemas, conformed dimensions) that dashboards and machine-learning use cases sit on.
  • Put data-quality checks and lineage capture into every pipeline, and keep the conceptual, logical and physical model documentation current.
  • Work with business owners to reconcile figures and define KPIs, and help write the monthly progress report.

What you need

  • Strong Python and SQL, used in production
  • Spark (Databricks or Hadoop) and BigQuery, and comfort with both data-lake and warehouse patterns
  • Hands-on experience with at least two of Talend, Informatica, NiFi and Denodo
  • Extracting and capturing changes from MS SQL Server, Oracle, MySQL and PostgreSQL
  • Ingesting from APIs, files (SFTP, Excel, CSV) and open data, in batch and streaming
  • Dimensional modelling: star schemas, conformed dimensions, slowly changing dimensions, and the ability to document models at conceptual, logical and physical level
  • Orchestration, monitoring, retries, data-quality checks and lineage
  • Profiling, reconciliation, defining KPIs with business users

Good to have

  • dbt, Airflow, Dataflow, Iceberg
  • Google Professional Data Engineer or Databricks certificatio
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