Coforge U.K. Ltd

Amazon Connect Engineer About the Role: We seek a seasoned Senior Cloud Engineer with a strong focus on Amazon Connect to join our team. You will be pivotal in designing, developing, and maintaining robust contact center solutions. Your AWS services and infrastructure expertise will be instrumental in delivering high-performance, scalable, and secure contact center environments. Responsibilities: Technical Leadership: Provide technical guidance and mentorship to the engineering team in designing and implementing contact center solutions. AWS Expertise: Demonstrate deep knowledge of AWS services (Lambda, DynamoDB, S3, SNS, SNS, Lex, Bedrock, API Gateway, Pinpoint, etc.) to architect and optimize cloud infrastructure for contact center workloads. Amazon Connect Mastery: Possess an in-depth understanding of Amazon Connect features, including contact flows, routing, analytics, and integrations. Infrastructure as Code: Utilize AWS CDK or other infrastructure as code tools to automate and manage cloud resources efficiently. Performance Optimization: Identify and implement strategies to optimize contact center performance, scalability, and cost-effectiveness. Troubleshooting: Diagnose and resolve complex technical issues related to contact center infrastructure and applications. Automation: Develop and implement automation scripts to streamline operational tasks and improve efficiency. Security: Ensure adherence to security best practices and compliance standards. Qualifications: Proven experience as a Senior Cloud Engineer with a strong focus on contact center solutions. In-depth knowledge of AWS services and architecture. Hands-on experience with Amazon Connect and its integration with other AWS services. Proficiency in Scripting languages (Python, Bash, etc.) for automation. Strong problem-solving and troubleshooting skills. Ability to lead and mentor junior engineers. Preferred Qualifications: AWS certifications (Solutions Architect, DevOps Engineer, etc.). Experience with performance and load testing of Managed services Knowledge of contact center industry standards and best practices.

Data Modeler Design, develop, and maintain conceptual, logical, and physical data models to support business needs and objectives across various domains. Collaborate with stakeholders, including business analysts, data architects, and application developers, to gather and define data requirements. Create and optimize database schemas and data structures to ensure data consistency, accuracy, and integrity. Implement best practices for data modelling, including normalization, de-normalization, and dimensional modelling, to support efficient data storage and retrieval. Lead the design and implementation of complex data architectures for enterprise-level systems, ensuring scalability and performance. Develop and maintain data dictionaries, data lineage documentation, and metadata repositories to support data governance and standardization efforts. Perform data mapping and data profiling to ensure data quality and consistency across systems and environments. Work closely with ETL developers to design data integration strategies, ensuring seamless data flow between source and target systems. Utilize data modelling tools such as Erwin, PowerDesigner, or similar to create, modify, and maintain data models and data flow diagrams. Provide guidance and mentorship to junior data modelers and data engineers, promoting best practices and methodologies. Ensure compliance with data governance policies, industry standards, and regulatory requirements for data management and privacy. Support data migration efforts by creating detailed data mapping and transformation rules between Legacy and target systems. Experience in modelling for various databases, including relational (SQL Server, Oracle, MySQL) and NoSQL (MongoDB, Cassandra) systems. Collaborate with BI and reporting teams to ensure data models support advanced analytics, reporting, and visualization needs. Analyze and optimize existing data models and database performance, identifying bottlenecks and making recommendations for improvement. Maintain detailed documentation of data models, schemas, and changes to ensure transparency and future scalability.