Analytics Engineer

The Analytics Engineer combines data engineering and data analysis to provide dedicated analytics support for the Customer Care organization while reporting to the Corporate Business Intelligence team. This role manages the full lifecycle of Customer Care data—from sourcing, integrating, and transforming data across multiple support systems into reliable analytical datasets, to analyzing that data and delivering actionable insights that support operational and business decisions.  Working closely with Customer Care leaders and the Business Intelligence team, this role builds and maintains the data foundations, reporting, dashboards, and analytical solutions needed to improve Contact Center performance and customer experience. The Analytics Engineer proactively identifies data and reporting needs, recommends analytical solutions, and drives continuous improvement through scalable, reliable, and actionable data. Key Responsibilities Data Engineering & Data Management Design, build, and maintain analytical tables and datasets used for reporting, dashboards, and forecasting. Ingest and transform raw data from contact center systems (e.g., ACD, CRM, ticketing, chat, email) into clean, structured datasets. Ensure data accuracy, consistency, and integrity across multiple source systems. Optimize table structures and queries for performance and scalability. Document data definitions, transformation logic, and assumptions to support transparency and reuse. Develop and apply AI models to analyze customer contact transcripts, surveys, and other feedback sources, identifying key trends, themes, and actionable insights that inform business and customer experience decisions. End-to-End Contact Center Data & Analytics Own the end-to-end analytics workflow for Contact Center data: source systems → ELT to Datalake -> dim/fact table -> transformed analytical tables → reports/dashboards → insights. Work with data from multiple Contact center platforms, including ACD, CRM, ticketing systems, chat, and other digital interaction tools. Translate reporting and analytics needs into scalable data models and analytical outputs. Ensure that insights are built on consistent, well-defined, and trusted data sources. Contact Center Performance Analysis & Insights Analyze Contact Center performance across key operational metrics, including service level, ASA, AHT, contact volume, abandonment, occupancy, shrinkage, and adherence. Develop and maintain recurring and ad-hoc reports and dashboards for operational teams and leadership. Identify trends, risks, and opportunities impacting efficiency and service performance. Perform root cause analysis to explain performance gaps and operational challenges. Measure the impact of operational initiatives using before-and-after analysis and ongoing performance tracking. Customer Behavior & Experience Analytics Analyze customer interaction data across channels to understand contact drivers, behavior patterns, and end-to-end journeys. Track and interpret customer experience metrics such as CSAT, NPS, FCR, repeat contacts, and transfers. Identify drivers of customer dissatisfaction, high contact volume, long handle times, and repeat contacts. Translate analytical findings into insights that support improvements in self-service, agent workflows, and process design. Build dashboards and analyses that connect customer behavior to operational and business outcomes. Forecasting & Volume Modeling Develop, maintain, and refine forecasting models to predict contact volume by channel and contact reason. Validate forecasts against actual results and identify drivers of variance. Continuously improve forecast accuracy using historical data and trend analysis. Continuous Improvement Identify opportunities to automate manual reporting and improve data workflows. Recommend enhancements to data architecture and analytical models to better support contact center analytics. Contribute to data governance, documentation standards, and analytics best practices.