Data Engineer - Automation & AI
Job Title: Data Engineer – Automation & AI
Location: UAE – Abu Dhabi or Dubai
Engagement: Full-time (On-site)
Contract duration: 3 or 6 months + possible extensions
Start date: February 2026
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.
Responsibilities
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
