Data Engineer, Deriv

Data Engineer
United Arab Emirates, Dubai
October 6, 2026
Full Time

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Job Description

Deriv is hiring a data engineer that will help improve the data platform used by Deriv to manage trading, product analysis, compliance, business intelligence, and AI/ML applications in its fintech network around the world. The job requires creating stable data pipelines that ensure the delivery of reliable and fresh data for the important business processes. The main responsibilities include the development of the ETL/ELT pipelines for batch and streaming processes that incorporate data quality control, observability, freshness checks, schema drift detection, lineage management, and automated alerting.

Experience: 2–8 years of experience in data engineering, with hands-on experience building and maintaining production data pipelines.

Qualification: Bachelor’s Degree/Master’s Degree (Preferred)

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Responsibilities

  • Build ETL/ELT pipelines both in batch mode and in real time leveraging AI-based development.
  • Automate data quality validation, data freshness verification, schema drift detection, lineage, and alerting capabilities.
  • Develop and manage data contracts for SLAs, SLOs, schema agreement, and alignment of producers and consumers.
  • Ensure warehouse performance and cost effectiveness through optimized queries, partitioning, clustering, and orchestration.
  • Create scalable data models, semantic layer, and abstractions to support analytics, compliance, AI/ML, and BI requirements.

Qualifications

  • 2-8 years of experience in data engineering, experience in designing and managing data pipelines in production.
  • Experience in using GCP and BigQuery, experience in cloud data processing and optimization of warehouse.
  • Skills in programming in Python and SQL, experience in batch and stream processing.
  • Experience in working with Airflow, some experience in dbt or Dataform.
  • Knowledge of data modelling concepts such as the Kimball star schema, Data Vault, or Medallion.

Preferred Qualifications

  • Hands-on experience with Kafka or Pub/Sub architecture and event-based data processing systems.
  • Understanding of data governance, data contract, PII management, access control, auditing, and compliance issues.
  • Hands-on experience with feature pipelines and production AI/ML.
  • Hands-on experience with data observability, data schema management, data lineage, anomaly detection, and automated quality management.
  • Hands-on experience with CI/CD, pipeline versioning, peer review, and AI-driven development process.

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