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Data Engineering Unit Head
TheData Engineering Unit Head will lead the development and optimization of the company's enterprise data infrastructure. This role is responsible for designing, building, and maintaining scalable data pipelines, ensuring high data availability, and implementing cutting-edge technologies for data processing. The ideal candidate will have deep expertise in cloud and on-premise data platforms, big data frameworks, and real-time data processing, with a strong focus on engineering best practices.
Key Responsibilities:Data Architecture & Engineering:
Design, build, and maintain scalable, fault-tolerant, and high-performance data pipelines to support analytics, reporting, and digital banking initiatives.
Architect and implement modern data frameworks, including data lakes, data mesh, and event-driven architectures.
Develop and optimize ETL/ELT processes using Apache Spark, Kafka, Airflow, and cloud-native services.
Lead the adoption of real-time data streaming solutions for fraud detection, risk management, and customer insights.
Implement data partitioning, indexing, and storage optimization techniques to enhance performance and scalability.
Cloud & Infrastructure Management
Oversee the migration and management of big data solutions on cloud platforms (AWS, Azure, GCP).
Implement Infrastructure-as-Code (IaC) using Terraform, CloudFormation, or ARM templates for automated deployments.
Ensure high availability, scalability, and disaster recovery strategies for data platforms.
Monitor system performance, troubleshoot bottlenecks, and continuously improve data reliability and efficiency.
Data Governance & Security
Establish and enforce data engineering best practices, ensuring data integrity, consistency, and security.
Implement role-based access controls (RBAC), encryption, and audit logging to comply with BSP and financial regulations.
Collaborate with security teams to detect and prevent data breaches, ensuring compliance with GDPR, AML, and other industry standards.
Team & Stakeholder Collaboration
Lead and mentor a team of data engineers, software developers, and DevOps professionals.
Work closely with Data Science, AI, and IT teams to build an advanced analytics platform.
Collaborate with product and business units to align engineering solutions with banking requirements.
Qualifications & Experience
Bachelor’s or Master’s degreein Computer Science, Data Engineering, or a related field.
15+ years of experiencein data engineering, with at least5 years in a leadership role.
Expertise inbig data frameworks(Apache Spark, Hadoop, Kafka, Flink).
Strong programming skills inPython, Scala, Java, or Go for data processing.
Hands-on experience withSQL, NoSQL databases (MongoDB, Cassandra), and data warehousing.
Deep knowledge ofcloud-based data platforms(AWS Redshift, Azure Synapse, Google BigQuery).
Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for data deployments.
Strong understanding of distributed systems, microservices architecture, and data APIs.
Certifications inCloud, Data Engineering, or Agile Methodologies are a plus.
TheData Engineering Unit Head will lead the development and optimization of the company's enterprise data infrastructure. This role is responsible for designing, building, and maintaining scalable data pipelines, ensuring high data availability, and implementing cutting-edge technologies for data processing. The ideal candidate will have deep expertise in cloud and on-premise data platforms, big data frameworks, and real-time data processing, with a strong focus on engineering best practices.
Key Responsibilities:Data Architecture & Engineering:
Design, build, and maintain scalable, fault-tolerant, and high-performance data pipelines to support analytics, reporting, and digital banking initiatives.
Architect and implement modern data frameworks, including data lakes, data mesh, and event-driven architectures.
Develop and optimize ETL/ELT processes using Apache Spark, Kafka, Airflow, and cloud-native services.
Lead the adoption of real-time data streaming solutions for fraud detection, risk management, and customer insights.
Implement data partitioning, indexing, and storage optimization techniques to enhance performance and scalability.
Cloud & Infrastructure Management
Oversee the migration and management of big data solutions on cloud platforms (AWS, Azure, GCP).
Implement Infrastructure-as-Code (IaC) using Terraform, CloudFormation, or ARM templates for automated deployments.
Ensure high availability, scalability, and disaster recovery strategies for data platforms.
Monitor system performance, troubleshoot bottlenecks, and continuously improve data reliability and efficiency.
Data Governance & Security
Establish and enforce data engineering best practices, ensuring data integrity, consistency, and security.
Implement role-based access controls (RBAC), encryption, and audit logging to comply with BSP and financial regulations.
Collaborate with security teams to detect and prevent data breaches, ensuring compliance with GDPR, AML, and other industry standards.
Team & Stakeholder Collaboration
Lead and mentor a team of data engineers, software developers, and DevOps professionals.
Work closely with Data Science, AI, and IT teams to build an advanced analytics platform.
Collaborate with product and business units to align engineering solutions with banking requirements.
Qualifications & Experience
Bachelor’s or Master’s degreein Computer Science, Data Engineering, or a related field.
15+ years of experiencein data engineering, with at least5 years in a leadership role.
Expertise inbig data frameworks(Apache Spark, Hadoop, Kafka, Flink).
Strong programming skills inPython, Scala, Java, or Go for data processing.
Hands-on experience withSQL, NoSQL databases (MongoDB, Cassandra), and data warehousing.
Deep knowledge ofcloud-based data platforms(AWS Redshift, Azure Synapse, Google BigQuery).
Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for data deployments.
Strong understanding of distributed systems, microservices architecture, and data APIs.
Certifications inCloud, Data Engineering, or Agile Methodologies are a plus.
Data Engineering Unit Head
TheData Engineering Unit Head will lead the development and optimization of the company's enterprise data infrastructure. This role is responsible for designing, building, and maintaining scalable data pipelines, ensuring high data availability, and implementing cutting-edge technologies for data processing. The ideal candidate will have deep expertise in cloud and on-premise data platforms, big data frameworks, and real-time data processing, with a strong focus on engineering best practices.
Key Responsibilities:Data Architecture & Engineering:
Design, build, and maintain scalable, fault-tolerant, and high-performance data pipelines to support analytics, reporting, and digital banking initiatives.
Architect and implement modern data frameworks, including data lakes, data mesh, and event-driven architectures.
Develop and optimize ETL/ELT processes using Apache Spark, Kafka, Airflow, and cloud-native services.
Lead the adoption of real-time data streaming solutions for fraud detection, risk management, and customer insights.
Implement data partitioning, indexing, and storage optimization techniques to enhance performance and scalability.
Cloud & Infrastructure Management
Oversee the migration and management of big data solutions on cloud platforms (AWS, Azure, GCP).
Implement Infrastructure-as-Code (IaC) using Terraform, CloudFormation, or ARM templates for automated deployments.
Ensure high availability, scalability, and disaster recovery strategies for data platforms.
Monitor system performance, troubleshoot bottlenecks, and continuously improve data reliability and efficiency.
Data Governance & Security
Establish and enforce data engineering best practices, ensuring data integrity, consistency, and security.
Implement role-based access controls (RBAC), encryption, and audit logging to comply with BSP and financial regulations.
Collaborate with security teams to detect and prevent data breaches, ensuring compliance with GDPR, AML, and other industry standards.
Team & Stakeholder Collaboration
Lead and mentor a team of data engineers, software developers, and DevOps professionals.
Work closely with Data Science, AI, and IT teams to build an advanced analytics platform.
Collaborate with product and business units to align engineering solutions with banking requirements.
Qualifications & Experience
Bachelor’s or Master’s degreein Computer Science, Data Engineering, or a related field.
15+ years of experiencein data engineering, with at least5 years in a leadership role.
Expertise inbig data frameworks(Apache Spark, Hadoop, Kafka, Flink).
Strong programming skills inPython, Scala, Java, or Go for data processing.
Hands-on experience withSQL, NoSQL databases (MongoDB, Cassandra), and data warehousing.
Deep knowledge ofcloud-based data platforms(AWS Redshift, Azure Synapse, Google BigQuery).
Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for data deployments.
Strong understanding of distributed systems, microservices architecture, and data APIs.
Certifications inCloud, Data Engineering, or Agile Methodologies are a plus.
TheData Engineering Unit Head will lead the development and optimization of the company's enterprise data infrastructure. This role is responsible for designing, building, and maintaining scalable data pipelines, ensuring high data availability, and implementing cutting-edge technologies for data processing. The ideal candidate will have deep expertise in cloud and on-premise data platforms, big data frameworks, and real-time data processing, with a strong focus on engineering best practices.
Key Responsibilities:Data Architecture & Engineering:
Design, build, and maintain scalable, fault-tolerant, and high-performance data pipelines to support analytics, reporting, and digital banking initiatives.
Architect and implement modern data frameworks, including data lakes, data mesh, and event-driven architectures.
Develop and optimize ETL/ELT processes using Apache Spark, Kafka, Airflow, and cloud-native services.
Lead the adoption of real-time data streaming solutions for fraud detection, risk management, and customer insights.
Implement data partitioning, indexing, and storage optimization techniques to enhance performance and scalability.
Cloud & Infrastructure Management
Oversee the migration and management of big data solutions on cloud platforms (AWS, Azure, GCP).
Implement Infrastructure-as-Code (IaC) using Terraform, CloudFormation, or ARM templates for automated deployments.
Ensure high availability, scalability, and disaster recovery strategies for data platforms.
Monitor system performance, troubleshoot bottlenecks, and continuously improve data reliability and efficiency.
Data Governance & Security
Establish and enforce data engineering best practices, ensuring data integrity, consistency, and security.
Implement role-based access controls (RBAC), encryption, and audit logging to comply with BSP and financial regulations.
Collaborate with security teams to detect and prevent data breaches, ensuring compliance with GDPR, AML, and other industry standards.
Team & Stakeholder Collaboration
Lead and mentor a team of data engineers, software developers, and DevOps professionals.
Work closely with Data Science, AI, and IT teams to build an advanced analytics platform.
Collaborate with product and business units to align engineering solutions with banking requirements.
Qualifications & Experience
Bachelor’s or Master’s degreein Computer Science, Data Engineering, or a related field.
15+ years of experiencein data engineering, with at least5 years in a leadership role.
Expertise inbig data frameworks(Apache Spark, Hadoop, Kafka, Flink).
Strong programming skills inPython, Scala, Java, or Go for data processing.
Hands-on experience withSQL, NoSQL databases (MongoDB, Cassandra), and data warehousing.
Deep knowledge ofcloud-based data platforms(AWS Redshift, Azure Synapse, Google BigQuery).
Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for data deployments.
Strong understanding of distributed systems, microservices architecture, and data APIs.
Certifications inCloud, Data Engineering, or Agile Methodologies are a plus.