Data Engineer Contact Center Experience

Role Summary

The Contact Center Data Engineer will build and maintain the data pipelines, models and analytical datasets required to understand and optimize Assurant's customer interactions across voice, IVR, routing, agent desktop, recording, digital and operational systems.

The goal is to create a reliable Contact Center Data Foundation capable of supporting operational reporting, customer journey analytics, AI/ML and real-time decisioning.

Key Responsibilities

- Design and build pipelines from:
- Cisco ICM/UCCE
- CUIC
- CVP
- Finesse
- CUCM
- NICE
- IVR
- CRM
- Workforce Management
- Customer/claims/policy systems
- Digital channels.

- Ingest:
- Call Detail Records
- Routing events
- Agent states
- Queue data
- IVR navigation
- Dispositions
- Transfers
- Recording metadata
- Quality scores
- Speech/transcription data.

- Build unified interaction-level datasets.
- Create customer journey identifiers across platforms.
- Develop ETL/ELT pipelines.
- Design data models for contact-center analytics.
- Implement data-quality rules.
- Develop near-real-time pipelines for operational dashboards.
- Support AI/ML feature engineering.

- Enable analytics including:
- FCR
- AHT




- transfer rate
- abandonment
- repeat contacts
- containment
- customer effort
- agent occupancy
- service level.

- Partner with analysts/data scientists.
- Implement lineage, cataloging and governance.
- Optimize data processing cost and performance.
- Build APIs/data services where appropriate.
- Establish monitoring and alerting for pipeline failures.

Required Technical Skills

- SQL
- Python
- ETL/ELT
- Data modeling
- APIs
- Batch and streaming pipelines
- Cloud data platforms
- Data warehousing/lakehouse concepts.

Contact Center Experience Required

Knowledge of:

- Cisco ICM/UCCE data
- Call Detail Records
- Agent state data
- Routing and queue events
- IVR data
- NICE recording metadata
- Contact-center KPIs.

Preferred Skills

- AWS/Azure/GCP
- Snowflake
- Databricks
- Kafka/Kinesis
- Spark
- Power BI/Tableau
- dbt
- Airflow
- Contact-center speech analytics.

Success Measures

- Data completeness
- Data accuracy
- Pipeline SLA
- Data latency
- Contact-center KPI reconciliation
- Reporting availability
- Reduction in manual reporting
- Data-quality incidents

📌 Data Engineer (Contact Center Exp) (India)
🏢 Tecnoprism
📍 India