Senior Data Engineer-Redshift Spectrum & ( Retail,Corporate, SME Banking)

Urgent requirement for Senior Data Engineer with experience in Redshift Spectrum & ( Retail Banking ,Corporate Banking & SME Banking) in banking domain is required for our banking clients in Abu Dhabi ,UAE

Design and develop enterprise data warehouses, data marts, and analytical datasets.--MustExpertise in Kimball Dimensional Modeling, Star Schema,Redshift spectrum, Snowflake Schema, Fact & Dimension Modeling, and SCD implementation--MustBuild scalable ETL/ELT pipelines using AWS services (S3, Redshift, Glue, Lambda) and Apache Airflow.--MustOptimize SQL workloads, automate data processing, and ensure high-performance data delivery.--MustPartner with Data Science and AI/ML teams to create feature datasets, analytical base tables (ABTs), and model-scoring data.--MustStrong experience in Retail, Corporate, or SME Banking data domains.--Must

Role Overview

We are seeking an experienced Senior Data Engineer to design, develop, and maintain enterprise-scale data platforms that support Advanced Analytics, Artificial Intelligence (AI), and Machine Learning (ML) initiatives. The ideal candidate will have strong expertise in data warehousing, data modeling, ETL/ELT development, cloud technologies, and banking domain data. The role involves building scalable data pipelines, developing analytical data marts, and enabling data-driven decision-making across Retail, Corporate, and SME Banking environments. Key ResponsibilitiesData Engineering & Data Architecture

• Design and implement scalable data pipelines and data warehouse solutions.


• Develop conceptual, logical, and physical data models for analytical and reporting use cases.


• Build and maintain dimensional models, fact tables, dimension tables, and data marts.


• Design Star Schema, Snowflake Schema, and Data Vault architectures where appropriate.


• Define data standards, metadata, data lineage, and source-to-target mappings.


• Advanced SQL development and optimization.


• Data Warehousing and Data Mart design.


• ETL/ELT pipeline development.


• Kimball Data Modeling.


• Star Schema and Snowflake Schema design.


• SCD Type 1, Type 2, and Type 3 implementation. 

Data Pipeline Development

• Develop and optimize ETL/ELT processes using SQL, Python, and cloud-native services.


• Build and maintain daily and monthly analytical data marts.


• Implement incremental, snapshot, and full-load processing strategies.


• Ensure high-performance data processing for large-scale datasets.


• Perform query tuning, database optimization, and workload management.

Cloud & Orchestration

• Develop and manage data solutions on AWS cloud platforms.


• Build and monitor workflows using Apache Airflow.


• Manage Amazon Redshift environments, scheduled workloads, and performance optimization.


• Implement data quality validations, monitoring, alerting, and incident management processes.

Analytics & AI Enablement

• Collaborate with Data Scientists and ML Engineers to create feature-ready datasets.


• Develop Analytical Base Tables (ABTs), feature stores, and model scoring datasets.


• Support AI/ML use cases such as customer segmentation, churn prediction, fraud detection, credit risk, and recommendation systems.


• Enable production-grade data solutions for model training, validation, and monitoring.

Data Governance & Quality

• Implement data governance, security, and compliance standards.


• Maintain data lineage, business glossaries, and metadata documentation.


• Define and monitor data quality metrics including completeness, accuracy, consistency, and timeliness.


• Ensure secure handling of sensitive and regulated data assets. 

Required Qualifications

• Bachelor's Degree in Computer Science, Information Systems, Engineering, Statistics, Mathematics, or related discipline.


• Master's degree in Data Science, Analytics, or a related field is preferred.


• 7+ years of experience in Data Engineering, Data Warehousing, or Analytics Engineering.


• Minimum 4+ years of experience in Banking or Financial Services domain.


• Proven experience designing and developing enterprise-scale data warehouse and analytics solutions. 

Required Technical SkillsData Engineering

• Advanced SQL development and optimization.


• Data Warehousing and Data Mart design.


• ETL/ELT pipeline development.


• Kimball Data Modeling.


• Star Schema and Snowflake Schema design.


• SCD Type 1, Type 2, and Type 3 implementation. 

Cloud & Big Data

• AWS S3


• Amazon Redshift


• AWS Glue


• AWS Lambda


• CloudWatch


• IAM Fundamentals 

Orchestration

• Apache Airflow (DAGs, Scheduling, Monitoring, Backfills)


• Workflow Automation


• Pipeline Monitoring & Alerting 

Programming

• Python


• Pandas


• PySpark (Basic to Intermediate)


• Data Processing & Automation 

Databases

• MS SQL Server


• T-SQL


• Stored Procedures


• Performance Tuning


• Indexing & Query Optimization 

Banking Domain ExpertiseStrong understanding of:

• Retail Banking


• Corporate Banking


• SME Banking


• Core Banking Systems


• Customer & Account Data Models


• Credit Risk and Regulatory Reporting


• IFRS9, PD, LGD, EAD concepts


• Banking Analytics & MIS Reporting 

Key Competencies

• Strong analytical and problem-solving skills.


• Excellent stakeholder management and communication.


• Expertise data quality and governance practices.


• Ability to collaborate with business, analytics, AI, and technology teams.


• Ownership mindset with focus on accuracy and delivery