GSSTech Group
banking
DataEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Engineer at GSSTech Group. Skills: Real-time streaming, Data Warehousing, Data Modeling, Data Architecture. Design scalable data platforms. Manage enterprise data warehousing”
What You'll Achieve.
Ensure data processing SLAs are maintained
Industry & Context.
problem-solving mindset; analytical mindset
What They're Looking For.
Must Have
Enterprise Data Warehousing (EDW), Data Modeling, Data Lake architectures, Data Vault modeling, SQL, database design skills, relational databases, non-relational databases, Apache Flink, Apache Kafka, Java, Real-time streaming architectures
Nice to Have
Banking or financial services domain experience, digital banking, payments, analytics, enterprise data transformation programs, cloud-based data platforms, big data ecosystems
What You'll Do.
Design scalable data platforms
Manage enterprise data warehousing
Build streaming pipelines
Develop modern data architectures
Build and manage data ecosystems
Support real-time analytics
Support regulatory reporting
Support business intelligence
Support digital banking products
Build scalable data models
Implement Raw Data Vault
Implement Data Warehouse
Define data modeling standards
Design real-time streaming pipelines
Build low-latency architectures
Handle real-time ingestion
Transform large-scale datasets
Enrich large-scale datasets
Process large-scale datasets
Recommend data storage technologies
Implement data storage technologies
Define metadata management
Define business glossary
Define data ownership
Define derivation logic
Drive data quality initiatives
Drive data profiling initiatives
Drive governance initiatives
Drive master data management
Establish naming conventions
Establish data definitions
Establish documentation standards
Establish change management standards
Collaborate with business teams
Collaborate with analytics teams
Collaborate with engineering teams
Deliver scalable data solutions
Liaise with operational teams
Liaise with support teams
Ensure data processing SLAs
Support adoption of modern technologies
Support adoption of banking models
How You'll Work.
Team & Collaboration
Collaborate with business teams; Collaborate with analytics teams; Collaborate with engineering teams; Liaise with operational teams; Liaise with support teams; Guide developers; Guide data modelers
Communication Scope
Excellent communication skills
Full Job Description
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. **Requirements** ### Enterprise 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. ### Real-Time Data Streaming * 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. ### Data Architecture & Governance * 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 ### Stakeholder & Delivery Management * Collaborate with business, analytics, and engineering teams to deliver scalable data solutions.
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