Job description
We are looking for an experienced Senior Data Engineer / Data Architect with strong expertise in Enterprise Data Platforms, Data Warehousing, Real-Time Streaming, and Big Data technologies.
The ideal candidate will have strong hands-on experience designing scalable data architectures, enterprise data models, real-time data pipelines, and modern data platforms using technologies such as Kafka, Flink, Java, and Big Data ecosystems.
The role requires deep expertise in data modeling, data architecture principles, EDW design, metadata management, data governance, and working with large-scale banking and enterprise data environments.
Key Responsibilities: • Design, develop, and manage Enterprise Data Warehouse (EDW) models including Conceptual, Logical, Physical, and Virtual data models.
• Define and implement enterprise-level data architecture strategies aligned with business and technology requirements.
• Design scalable data storage and consumption models across structured, semi-structured, and unstructured data environments.
• Develop and maintain data models including: Data Vault Data Lake Enterprise Data Warehouse Data Marts • Define data architecture standards, principles, patterns, and best practices across enterprise data platforms.
• Design and optimize real-time streaming data solutions using technologies such as Apache Kafka, Apache Flink, and Java-based streaming frameworks.
• Build and maintain scalable data pipelines supporting Digital Products, Data Analytics, and enterprise reporting requirements.
• Evaluate and recommend relational and non-relational data storage technologies including: RDBMS NoSQL databases Big Data platforms Document databases • Provide technical guidance to data engineers, developers, modelers, and data administrators on data design and implementation.
• Drive adoption of modern data warehouse technologies and emerging data engineering practices.
• Collaborate with business stakeholders, analytics teams, application teams, and operational teams to define data requirements and solutions.
• Establish and maintain metadata management practices including: Business glossary Data definitions Golden source identification Data ownership Derivation logic Data lineage • Define and improve data quality, profiling, governance, mining, and master data management processes.
• Establish standards for: Data naming conventions Data definitions Documentation Ownership Change management procedures • Work with operational and support teams to ensure data processing SLAs are defined, monitored, and achieved.
Required Technical Skills: • Strong experience in Data Architecture and Enterprise Data Warehouse design.
• Strong understanding of data modeling methodologies, design patterns, and data warehouse principles.
• Hands-on experience with: Conceptual Data Modeling Logical Data Modeling Physical Data Modeling Enterprise Data Modeling • Strong experience designing: Data Lakes Data Warehouses Data Vault Models Data Marts • Strong hands-on experience with Real-Time Streaming technologies: Apache Kafka Apache Flink Java-based streaming applications • Strong programming experience in Java for data engineering and streaming solutions.
• Experience designing and implementing scalable data pipelines.
• Strong understanding of: Batch processing Real-time processing Distributed systems Data integration patterns • Experience working with: SQL Relational databases NoSQL databases Big Data technologies • Strong understanding of data governance, metadata management, lineage, and data quality frameworks.
• Experience with enterprise-scale analytics and reporting platforms.
Preferred Skills: • Experience working in Banking / Financial Services data environments.
• Experience with Enterprise Data Warehouse implementations.
• Exposure to industry-standard banking data models.
• Experience with cloud-based data platforms.
• Knowledge of modern data engineering frameworks and architecture patterns.
• Experience working with Agile delivery methodologies.
Required Competencies: • Strong analytical and problem-solving skills.
• Ability to design enterprise-scale data solutions.
• Strong communication skills with technical and business stakeholders.
• Ability to provide technical leadership and guidance to engineering teams.
• Strong ownership mindset with focus on scalability, quality, and performance.
• Ability to work in complex enterprise environments with multiple stakeholders.
This job post has been translated by AI and may contain minor differences or errors.
