Job Opportunities API

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  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.  Experienc