Data Engineer at Mobile Digital Insights (MDI)

About the job

• In today's rapidly evolving financial landscape, staying ahead of the curve means embracing innovation and leveraging cutting-edge technologies. 
• Our clients cloud-native banking strategy, powered by AWS, is designed to revolutionize banking operations, enhance customer experiences, and drive business growth.
• Join the future of banking with our client's cloud-native strategy powered by AWS.

Key Components of Our Client’s Cloud-Native Banking Strategy:

• Microservices Architecture: Break down monolithic applications into independent, scalable services.
• Containerization: Use Docker and Kubernetes for efficient application deployment and management.
• Serverless Computing: Implement AWS Lambda to run code without provisioning or managing servers.
• DevOps Practices: Streamline development and deployment with CI/CD pipelines using AWS CodePipeline and CodeBuild.
• Data Analytics: Harness the power of AWS data services like Amazon Redshift and AWS Glue for real-time insights and decision-making.
• AI and ML Integration: Deploy advanced AI and ML models to enhance operational efficiency and customer engagement
• Join the dynamic Data team as an AWS Data Engineer, where you'll develop cutting-edge data products and data warehouse solutions in both on-premises and cloud environments using cloud-native services, platforms, and technologies.

Key Responsibilities:

Operational Delivery:

• Develop and maintain a Data Lake using AWS technologies such as S3 and QuickSight.
• Build data APIs and delivery services to support critical operational and analytical applications.
• Participate in the development, coding, testing, and deployment of workflow solutions.
• Implement unit testing for all deliverables to ensure successful deployment.
• Support and maintain data solutions.
• Design and develop data models using dimensional modeling and data vault techniques.
• Translate high-level requirements into detailed specifications, prototypes, and software deployments.
• Deliver incremental business value in each sprint/cycle.
• Conduct peer reviews within and across teams.
• Profile and analyze data sets.

Technical Leadership:

• Engage in the engineering community of practice.
• Share AWS knowledge and practical experience with the community.
• Contribute to the development of architectural principles and patterns.

Compliance:

• Ensure solutions adhere to organizational patterns, guidelines, and standards.
• Operate within project environments and participate in continuous improvement efforts.

Delivery Management:

• Participate in sprint planning, backlog grooming, retrospectives, demos, and PI planning.

Experience:

• Proven experience in developing cloud-based solutions.
• 2-3 years of experience designing and developing data pipelines for data ingestion or transformation using AWS technologies.
• Experience in developing data warehouses and data marts.
• Proficiency in Data Vault and dimensional modeling techniques.
• Experience working in a high-availability DataOps environment.
• Familiarity with automated data warehousing solutions is advantageous.
• Experience with orchestration tools like Apache Airflow.
• Knowledge of CI/CD practices and tools such as GitHub and dbt Core.

Qualifications:

• Bachelor’s Degree in Computer Science, Information Systems, Big Data, or related fields.
• AWS Data Engineer Certification is a plus.
• Relevant technical certifications are advantageous.