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Data Engineer - Automation & AI

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Riverflex

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Location:
United Arab Emirates , Abu Dhabi or Dubai

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

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Job Description:

Riverflex is partnering with a leading financial institution in the UAE on a strategic Proof of Concept focused on increasing data engineering productivity through automation and AI. We are seeking a Data Engineer – Automation & AI with strong GenAI and agent-based experience to own this PoC end-to-end, applying AI- and agent-assisted techniques to the design, migration, and development of AWS-based data pipelines. This role is explicitly focused on accelerating data engineering ways of working using AI, not solely on building pipelines. You will act as the internal lead on how AI should be applied in day-to-day data engineering, working closely with the partner’s Data Lead and embedded with the on-site engineering team. A key part of the scope is translating legacy SQL and stored procedures into modern AWS Glue pipelines, while defining practical AI patterns, tool and guardrails that scale beyond the PoC.

Job Responsibility:

  • Data engineering & pipeline delivery: Design, build, and evolve AWS Glue–based data pipelines using Spark and SQL
  • Translate legacy SQL scripts and stored procedures into AWS Glue pipelines
  • Ensure migrated and newly built pipelines meet agreed standards for correctness, performance, and maintainability
  • AI-driven engineering acceleration: Apply Generative AI and agent-based techniques to accelerate data engineering tasks, including code generation/refactoring, pipeline dev. and standardisation
  • Own the design and implementation of AI-assisted tooling that integrates directly into day-to-day engineering workflows
  • Codify successful patterns, reusable tools, and recommended ways of working for scaling beyond the PoC
  • AI tooling & experimentation: Work hands-on with Python and LLM APIs to build pragmatic, internal DE tools
  • Design effective prompts & interaction patterns for code generation & transformation
  • Evaluate and work with enterprise-grade AI platforms (e.g. AWS Bedrock, Azure AI Foundry) using GPT-4 / Claude-class models
  • Define practical rules of thumb and guardrails (e.g. where automation works, where it breaks down, where human intervention is required)
  • Collaboration & ways-of-working: Work closely with data and platform engineers to (dis)prove automation hypotheses and identify where AI adds real productivity gains vs. noise
  • Document outcomes and recommendations from the PoC and provide clear guidance on how AI should (and should not) be used in data engineering at scale

Requirements:

  • 6+ years of experience in data engineering or closely related engineering roles
  • Proven experience owning and shaping data engineering solutions, not only implementing individual pipelines
  • Strong hands-on experience with AWS-based data engineering, including: AWS Glue (jobs, transformations, orchestration), Spark (batch processing and transformations), Advanced SQL (complex logic, optimisation, performance tuning), End-to-end pipeline and workflow design
  • Solid (Python) engineering experience, including building reusable components and internal tooling
  • Demonstrated, practical experience applying Generative AI in engineering workflows, such as: Working with LLM APIs (e.g. AWS Bedrock, Azure AI Foundry, OpenAI), Prompt design for code generation, refactoring, and transformation, Understanding the limitations, failure modes, and risks of LLM-based automation
  • Experience designing AI-assisted engineering workflows or tools, for example: API-based services (e.g. FastAPI), MCP (or agent)-like orchestration patterns
  • Able to balance short-term PoC delivery with longer-term capability building
  • Experience in financial services or other regulated environments is a strong advantage
  • Ability to be based in the UAE for a minimum of 3 months, working full-time on-site (Abu Dhabi or Dubai)

Additional Information:

Job Posted:
January 10, 2026

Employment Type:
Fulltime
Work Type:
On-site work
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