The Data Engineer will design and develop scalable data architectures and real-time streaming pipelines, manage enterprise data ecosystems, and ensure data governance and quality, primarily within the banking sector.
We are looking for a highly skilled Data Engineer with strong expertise in real-time streaming technologies and enterprise data engineering to join a leading digital transformation initiative within the banking domain.
The ideal candidate should have hands-on experience in designing scalable data platforms, enterprise data warehousing, streaming pipelines, and modern data architectures using technologies such as Apache Flink, Kafka, and Java.
This role will be responsible for building and managing enterprise-grade data ecosystems that support real-time analytics, regulatory reporting, business intelligence, and digital banking products.
RequirementsEnterprise Data Engineering & Modeling
- Design, develop, and manage Enterprise Data Warehouse (EDW) models including:
- Conceptual
- Logical
- Physical
- Virtual data models
- Build scalable and optimized data models for enterprise analytics and reporting.
- Design and implement:
- Raw Data Vault
- Data Lake
- Data Warehouse
- Data Marts
- Define standards and best practices for data modeling methodologies and design patterns.
- Design and develop real-time streaming pipelines using:
- Apache Kafka
- Apache Flink
- Java
- Build low-latency, high-throughput streaming architectures for banking and financial systems.
- Handle real-time ingestion, transformation, enrichment, and processing of large-scale datasets.
- Recommend and implement suitable data storage technologies including:
- RDBMS
- NoSQL
- Big Data platforms
- Document Databases
- Define metadata management, data lineage, business glossary, ownership, and derivation logic.
- Drive data quality, profiling, governance, and master data management initiatives.
- Establish standards for:
- Naming conventions
- Data definitions
- Documentation
- Change management
- Collaborate with business, analytics, and engineering teams to deliver scalable data solutions.
- Liaise with operational and support teams to ensure data processing SLAs are maintained.
- Guide developers and data modelers on enterprise data architecture and best practices.
- Support adoption of modern data warehouse technologies and banking industry-standard data models.
- Strong experience in:
- Apache Flink
- Apache Kafka
- Java
- Real-time streaming architectures
- Expertise in:
- Enterprise Data Warehousing (EDW)
- Data Modeling
- Data Lake architectures
- Data Vault modeling
- Strong SQL and database design skills.
- Experience with relational and non-relational databases.
- Banking or financial services domain experience is highly preferred.
- Experience working on digital banking, payments, analytics, or enterprise data transformation programs.
- Exposure to cloud-based data platforms and big data ecosystems is a plus.
- Strong problem-solving and analytical mindset.
- Ability to work in large enterprise environments.
- Experience handling high-volume, mission-critical data systems.
- Excellent communication and stakeholder management skills.
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