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.
Bachelor’s degree in Data Science, Computer Science, Statistics, Information Systems, Business Analytics or equivalent experience.
3 – 5 years of experience in data analytics, analytics engineering, or a related role.
Strong SQL skills, including creating and maintaining tables, views, and transformations.
Experience working with data from contact center systems such as ACD, CRM, ticketing platforms, or chat tools (preferred).
Solid understanding of contact center metrics and performance drivers.
Experience with data visualization tools such as Domo, Tableau, Power BI, or similar.
Strong analytical thinking, attention to detail, and problem-solving skills.
Experience with ETL/ELT tools and data pipelines.
Preferred Qualifications
Experience with data warehousing concepts and basic dimensional modeling.
Experience working with large datasets and multiple source systems.
Exposure to cloud data platforms such as AWS, Snowflake, BigQuery.
Strong problem-solving and analytical skills.
Ability to translate complex data into clear, actionable insights.

Employment type
Full-time

Work arrangement
Hybrid