←BACK TO RESUME
Private Architecture
February 202416 weeks

AWS Connect Call Center Integration and AWS Infrastructure

Engineering Dossier

Achievement Log

2024-03-20

Led comprehensive Amazon Connect architecture research and enterprise implementation for large-scale call center operations

  • Architected and designed highly scalable call center infrastructure with advanced Lambda integrations, enterprise DynamoDB, S3, and sophisticated QuickSight analytics platform
  • Conducted strategic leadership meetings, presented comprehensive technical updates, and offered advanced strategic solutions for call flow optimization and customer experience enhancement
✨ Result:

40% improvement in call handling efficiency, real-time analytics implementation, and enhanced customer experience

Overview

Designed and implemented a scalable, secure call center system using AWS Connect, integrating various AWS services like AWS Lambda, AWS DynamoDB, AWS S3, AWS Kinesis Data Firehose, AWS EventBridge, AWS Simple Queue Service (SQS), and AWS QuickSight to enhance customer service and provide real-time data processing and analytics.

Core Technologies

AWS ConnectAWS VPC & SubnetsAWS LambdaAWS DynamoDBAWS S3AWS Kinesis Data FirehoseAWS QuickSightAWS EventBridgeAWS SQSAWS RDS

Implementation & Architecture

AWS Connect Configuration

Custom call flow logic, IVR, and speech-to-text integration for AWS Connect.

Execution Protocol

  1. Set up AWS Connect instance: Configure the cloud contact center for handling customer calls.
  2. Implement IVR: Integrate interactive voice response for routing customer queries.
  3. Integrate speech-to-text: Enable real-time speech recognition and data capture.

Lambda Functions

Used AWS Lambda for API integrations and data processing tasks.

Execution Protocol

  1. Develop Lambda functions: Automate tasks like data validation and API calls.
  2. Set up event triggers: Configure Lambda to be triggered by AWS Connect events.
  3. Optimize for concurrency: Ensure Lambda functions efficiently handle multiple tasks simultaneously.

DynamoDB Integration

Managed customer profiles and session data using AWS DynamoDB.

Execution Protocol

  1. Provision DynamoDB tables: Create tables for storing session information and customer profiles.
  2. Implement distributed locking: Use DynamoDB's conditional writes to handle race conditions.
  3. Configure data retention: Set TTL for session data to automatically expire after a specified time.

S3 Integration

Store call recordings, surveys, and logs in Amazon S3 for later analysis.

Execution Protocol

  1. Set up S3 buckets: Create storage for call recordings and survey results.
  2. Implement S3 event triggers: Automatically process or analyze data as it's stored.
  3. Enable versioning: Keep track of changes and updates to recordings and results.

Kinesis Data Firehose Setup

Stream real-time data from AWS Connect to Amazon S3 using Kinesis Data Firehose.

Execution Protocol

  1. Set up Kinesis stream: Configure data flow from AWS Connect to S3 via Firehose.
  2. Enable real-time processing: Stream data to S3 for further processing and analytics.
  3. Integrate with Lambda: Trigger Lambda functions for processing streamed data.

QuickSight Dashboard

Create real-time dashboards for operational insights and analytics.

Execution Protocol

  1. Connect S3 to QuickSight: Set up data sources for real-time visualization.
  2. Create analytical dashboards: Build dashboards for performance monitoring and insights.
  3. Share insights: Enable team access to dashboards for decision-making.

EventBridge Setup

Configure AWS EventBridge for event-driven architecture and workflows.

Execution Protocol

  1. Set up EventBridge bus: Create an event bus for routing AWS Connect events.
  2. Filter events: Use rules to filter and trigger specific actions based on event criteria.
  3. Integrate with Lambda: Trigger Lambda functions in response to specific events.

SQS Integration

Use AWS SQS to manage event queues for parallel processing.

Execution Protocol

  1. Provision SQS queues: Set up queues for handling high-volume events.
  2. Integrate with Lambda: Use SQS to trigger Lambda functions for processing events.
  3. Implement message delay: Ensure optimal processing times by delaying messages as necessary.

RDS Setup

Provision AWS RDS for relational data storage using PostgreSQL.

Execution Protocol

  1. Create PostgreSQL instance: Deploy a relational database for structured data storage.
  2. Configure backups: Set up automatic backups and scaling for high availability.
  3. Implement data integrity: Ensure data consistency using foreign keys and other relational database features.

Technical Skills

  • Amazon Connect
  • AWS Lambda
  • Amazon DynamoDB
  • Amazon S3
  • AWS Kinesis
  • Amazon QuickSight
  • Amazon EventBridge
  • Amazon SQS (Simple Queue Service)
  • Amazon SNS (Simple Notification Service)
  • Amazon Pinpoint
  • Amazon Connect Contact Flows
  • Amazon Connect Lex Integration
  • Amazon Connect Lambda Integration
  • Amazon Connect Global Resiliency
  • Amazon RDS
  • Amazon VPC
  • VPC Subnets & Routing
  • AWS IAM
  • PostgreSQL
  • Event-Driven Architecture (EDA)
  • Root Cause Analysis
  • Incident Response

Engineering Challenges

  • →Survey Flow Optimization
  • →Concurrency and Locking
  • →Real-Time Processing

Project Outcomes

  • ✓
    Enhanced Customer Service
  • ✓
    Operational Efficiency
  • ✓
    Improved Data Insights