Data Engineering Manager

* The Data Engineering Manager is a hands-on engineer responsible for building and maintaining enterprise data products, taking raw Bronze-layer data through cleansed and conformed Silver layers into Gold-layer datamarts that the ML and AI teams consume to build products. * Working at enterprise function level across Digital Banking, Credit Risk, Fraud and other data sources. * The role develops high-performance SQL and Python pipelines on AWS and Amazon Redshift, turns ML outputs into business intelligence and partners closely with Data Platforms teams to deliver governed, high-quality data in line with CBUAE regulatory requirements.

Job Overview:

• Data Pipeline & Datamart Development: Build and maintain Bronze-to-Silver-to-Gold pipelines and Gold-layer datamarts that deliver trusted, reusable data products for analytics and AI consumption.
• SQL & Python Engineering: Develop and tune high-performance SQL on Amazon Redshift and Microsoft SQL Server, including stored procedures, views, materialized views and functions, using Python for transformation and automation.
• Multi-source Data Integration: Integrate and conform enterprise data from digital onboarding, mobile and internet banking channels, credit risk, fraud and other sources into consistent, well-modelled datasets.
• ML & AI Data Enablement: Deliver feature-ready datasets to ML and AI teams and integrate model outputs into datamarts to create actionable business intelligence.
• Data Quality & Governance: Implement data quality checks, reconciliation and lineage to ensure accurate, well-governed data that meets CBUAE regulatory requirements.
• Platform & Squad Collaboration: Partner with Data Platforms teams and squad members to apply AWS engineering standards, CI/CD and agile practices across the data product lifecycle.

Experience & Competencies Required:

• 8+ years of experience in data engineering or data warehousing.
• Extensive experience in banking or financial services working with digital banking, credit risk and/or fraud data.
• Hands-on experience with AWS and Amazon Redshift.
• Experience building medallion-based datamarts for analytics and ML consumption.
• Bachelor’s Degree in Computer Science, Engineering, Information Technology or related discipline.
• AWS Certified Data Engineer – Associate (preferred).

Technical Skills Required:

• Advanced SQL on Amazon Redshift and Microsoft SQL Server (stored procedures, views, materialized views, functions, performance tuning)
• Python for data engineering and automation
• AWS data services (S3, Glue, Lambda, Step Functions, Athena, IAM, CloudWatch)
• Medallion architecture (Bronze, Silver, Gold layers)
• Dimensional data modelling (star schema, slowly changing dimensions)
• Digital banking, credit risk and fraud data domains
• ML/AI data enablement and feature-ready datasets
• Data quality, reconciliation and lineage
• Git, CI/CD and pipeline orchestration
• JIRA, Confluence and Agile delivery