What are the responsibilities and job description for the CCaaS Data Engineer -Need Only W2 Candidates position at Mega Cloud Lab?
Job Role: CCaaS Data Engineer -Need Only W2 Candidates
Location: Charlotte, NC (Onsite -2-3 days/week)
Responsibilities
Must have Experience 4 Years
Skills: CCaaS/CTI/CRM, IVA, IVR, Java, Microservices, Springboot, Kafka, AI/ML(Sarima/Prophet), Release Management(Maven/Git)
Qualifications
Location: Charlotte, NC (Onsite -2-3 days/week)
Responsibilities
Must have Experience 4 Years
Skills: CCaaS/CTI/CRM, IVA, IVR, Java, Microservices, Springboot, Kafka, AI/ML(Sarima/Prophet), Release Management(Maven/Git)
Qualifications
- 4 years of commercial software development experience.
- Design and implement scalable CCaaS and IVA solutions leveraging leading cloud and enterprise conversational AI/customer service solutions, including conversational IVR design, NLU/NLP modeling, intent and flow orchestration, webhook integrations, and speech-to-text/text-to-speech.
- Develop secure, resilient cloud infrastructure on major cloud service providers using services such as GKE, Cloud Run, Cloud Functions, Pub/Sub, Apigee, and BigQuery, implementing IAM, VPC design, encryption, multi-region high availability, and Infrastructure as Code (Terraform) to support enterprise-grade customer experience platforms.
- Implement and optimize CCaaS solutions, including ACD (Automatic Call Distribution), skills-based routing, dialer, omnichannel capabilities, and campaign management, ensuring scalable, secure, and compliant contact center operations.
- Experience in integrations and migrations leveraging CCaaS APIs and telephony capabilities, including CRM/CTI integrations, webhooks, SIP/WebRTC, security configuration, and transition from legacy contact center platforms to cloud-based solutions.
- Design, develop, and maintain data pipelines, models, and datasets to support CCaaS data platforms while ensuring data quality, reliability, security, and compliance.
- Experience building and integrating AI/ML solutions such as XGBoost, SARIMA, Prophet, prompt engineering, and TensorFlow into production systems.
- Understanding of the model lifecycle, including evaluations, hyperparameter tuning, ongoing monitoring, model governance, bias mitigation, and explainability.
- Experience with Agile development, CI/CD, DevOps, and observability.
- Hands-on experience with Kafka, relational databases, and/or NoSQL databases.
- Understanding of data structures, algorithms, and design patterns.
- Proactively identifies continuous improvement opportunities beyond the obvious.