Deep Light AI is a specialist AI and data consultancy dedicated to transforming the regional corporate landscape through bespoke, high-impact intelligent systems. We combine deep expertise in data engineering, cloud architecture, AI/ML platforms, and systems integration with a practical understanding of complex enterprise operations.
With deep roots across the financial services and banking sectors, we help major institutions bridge the gap between complex data infrastructure and actionable AI strategy. We don't build temporary fixes—we engineer secure, scalable, and resilient data foundations designed to power production AI at scale.
The OpportunityWe are seeking a Data Engineer with strong AWS and banking sector experience to join our core engineering squad in the UAE.
In this role, you will design, construct, and optimize scalable Lakehouse data solutions that power enterprise analytics and advanced machine learning models. Operating at the intersection of big data, cloud architecture, and MLOps, you will unify data warehousing and data lake capabilities to process complex, high-volume financial data while maintaining strict banking security, ACID compliance, and data governance standards.
What You'll Do- Build Next-Gen Lakehouse Architectures:
Design, build, and optimize scalable data solutions using Lakehouse architectures (Databricks, Delta Lake) to unify data warehousing and data lake capabilities on AWS - Develop High-Throughput Pipelines:
Build robust batch and real-time ETL/ELT pipelines using PySpark, Apache Kafka, and SQL to ingest and transform complex financial datasets from core banking, market data, and transactional systems - Optimize for Scale & Fin Ops:
Maximize query performance and storage efficiency across AWS services (S3, Redshift, EMR, Athena, Glue) using advanced partitioning, indexing, and parallel processing to drive down latency and cloud compute costs - Support Machine Learning & MLOps:
Partner with Data Scientists and MLOps Engineers to build automated feature engineering pipelines, track experiments (MLflow), containerize workflows (Docker, EKS), and deploy models to production (AWS Sage Maker) - Enforce Banking-Grade Governance:
Implement end-to-end data security, encryption, access controls, and audit trails to ensure 100% compliance with Central Bank data sovereignty and privacy mandates - Automate Everything:
Drive software engineering best practices by managing Infrastructure as Code (Terraform), authoring CI/CD pipelines (Git Hub Actions/Code Pipeline), and writing clean, well-tested Python code
- Hands-on data engineering experience directly within banking, financial services, or fintech environments, with a strong understanding of transactional datasets and regulatory compliance
- Deep technical command of core AWS data services (S3, Glue, EMR, Redshift) alongside distributed processing engines (Apache Spark / PySpark, Apache Kafka)
- Proven track record building and managing Lakehouse architectures (Databricks, Delta Lake) with expert knowledge of data modeling, schema evolution, and ACID principles
- High proficiency in Python and SQL (Scala is a plus), alongside practical experience with Docker, Kubernetes (EKS), Terraform, and Git workflows
- Excellent analytical problem-solving skills with the ability to articulate complex technical concepts clearly to cross-functional teams and client stakeholders
- AWS Certified Data Engineer, AWS Solutions Architect, or Databricks Certified Data Engineer
- Hands-on experience with streaming analytics frameworks (Apache Flink, Spark Streaming) for real-time fraud detection or trade processing pipelines
- Production experience with modern workflow schedulers like Apache Airflow or Prefect
As…
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