Data Engineer | Talent Bridge Dubai

talentbridgedubai; Dubai; On-site; AED 180,000 - 280,000; Full time

Job summary

talentbridgedubai is seeking an experienced data engineer to build robust, observable data pipelines powering research and production AI. You will partner with AI/ML, analytics, and product teams to ensure reliable on-time delivery and trustworthy data.

Responsibilities include architecting batch/stream ETL, enforcing data quality and governance, supporting ML workflows with DVC/MLflow, building feature stores and secure REST endpoints, and maintaining internal catalogs for self-service

Qualifications

• Skills: C++/Java, SQL/NoSQL, Pandas/NumPy, PySpark, Airflow, API development, Docker.
• MLOps: DVC, MLflow or W&B, model packaging/deployment fundamentals.
• Cloud: AWS SageMaker, Azure ML, or GCP AI experience.
• Nice to have: Unreal Engine exposure.
• Environment: Linux background for development and deployment.
• Education/Experience: Proven experience building reliable pipelines in production.

Responsibilities

• Architect and operate batch/stream pipelines (Airflow; Spark optional) for ETL/ELT.
• Model/manage schemas; enforce data quality and lineage/governance.
• Support ML workflows with DVC and MLflow or Weights & Biases.
• Build feature stores/data services; expose datasets via secure REST endpoints.
• Maintain documentation and internal catalogs; enable self-service analytics.

Skills

C++/Java programming

SQL/NoSQL

Pandas/NumPy

PySpark

Airflow

API development

ML/DL tooling

DVC

MLflow / Weights & Biases

Cloud: AWS SageMaker/Azure ML/GCP

Linux

Unreal Engine (nice to have)

Tools

Docker

Job description

Build robust, observable data pipelines that power research and production AI. Success means high pipeline reliability (on-time SLAs), strong data quality (validation & lineage), and enabling fast experimentation. You will partner with AI/ML, analytics, and product to make data trustworthy and usable.

Responsibilities

• Architect and operate batch/stream pipelines (Airflow; Spark optional) for ETL/ELT.
• Model/manage schemas; enforce data quality and lineage/governance.
• Support ML workflows with DVC (data versioning) and MLflow or Weights & Biases.
• Build feature stores/data services; expose datasets via secure REST endpoints.
• Maintain documentation and internal catalogs; enable self-service analytics.

Qualifications

• Skills: Programming in C++ or Java; SQL & NoSQL; Pandas/NumPy; PySpark; Airflow; API development; Docker.
• MLOps: DVC; MLflow or W&B; model packaging/deployment fundamentals.
• Cloud: AWS SageMaker, Azure ML, or GCP AI experience.
• Nice to have: Unreal Engine exposure.
• Environment: Solid Linux background for development and deployment.
• Education/Experience: Proven experience building reliable pipelines in production