Responsibilities
- Create and manage robust data pipelines for ingesting and transforming data from customer service systems, communication platforms, and external tools.
- Build and refine data warehouse structures to enable reporting, performance tracking, forecasting, and evaluation of customer experience programs.
- Set up automated checks and monitoring systems to detect data anomalies and ensure accuracy in critical datasets.
- Partner with business units to establish clear data definitions, service level expectations, and ownership frameworks for key metrics.
- Optimize data processing workflows and query performance for large-scale datasets including calls, chats, emails, and case records.
- Collaborate with platform and engineering teams to adopt standardized development practices for data systems.
- Develop internal tools and applications to support analytics, case tracking, alerts, quality assurance, and agent performance evaluation.
- Use programming languages like Python, Java, Golang, or JavaScript/TypeScript to build APIs and lightweight interfaces for data access.
- Work with product and operations teams to convert operational challenges into functional data solutions.
- Apply software engineering principles such as testing, code reviews, and continuous integration to ensure reliability of data systems.
- For Staff-level roles, lead the strategic direction of data platforms to promote reuse across regions and business units.
- Translate business questions into analytical frameworks in collaboration with product and operations stakeholders.
- Develop and maintain dashboards that track contact volume, handling time, quality metrics, and agent productivity.
- Conduct in-depth analysis of customer interaction trends and operational performance to identify root causes and recommend improvements.
- Define and standardize KPIs such as customer satisfaction, net promoter score, first contact resolution, and average handling time.
- Present insights using clear, non-technical language with actionable recommendations for business teams.
- Serve as a primary data contact for customer experience leaders, product teams, workforce management, and training units.
- Coordinate with data professionals to align on shared datasets, modeling standards, and reusable components.
- Guide less experienced engineers in best practices for data and software engineering.
- Support ongoing improvements in team processes including documentation, testing, and incident response.
- For Staff-level roles, help shape cross-functional data architecture to support long-term scalability.
Team
Part of the Customer Experience Group, this role supports data infrastructure and analytics for global operations teams handling customer interactions.
Responsibilities
- Design, build, and maintain scalable ETL/ELT pipelines to ingest, transform, and serve data from CEG systems (case management, telephony, chat, bots, QA tools, WFM, CRM) and third-party tools and logs
- Develop and optimize data models (e.g., warehouse tables, marts, views) that power CEG reporting, monitoring, forecasting, QA, and experimentation
- Implement validation checks, anomaly detection, and monitoring for key CEG datasets and metrics
- Work with stakeholders to define and enforce data definitions, SLAs, and ownership for critical tables and metrics
- Improve performance and cost efficiency of data jobs and queries (e.g., partitioning, indexing, query tuning, storage format optimization) for high-volume CEG data (calls, chats, emails, cases, events)
- Collaborate with data platform and engineering teams to standardize tooling and best practices (e.g., version control, CI/CD for data and services, code review, documentation, runbooks)
- Design and build data tools and internal products for CEG (e.g., self-service analytics, case monitoring dashboards, alerting systems, investigator tools, QA/review tools, agent performance views)
- Use languages such as Python, Java, Golang, or JavaScript/TypeScript to implement APIs, data services, and lightweight UIs that expose data in a usable way to agents, managers, and operations teams
- Work closely with CEG product managers and operations to translate operational pain points into concrete data tools, from problem framing to delivery and iteration
- Apply solid software engineering practices—testing, code reviews, CI/CD, observability—to data products so they are reliable, maintainable, and easy to extend
- At Staff level, define and drive the roadmap for key CEG data tools and platforms, ensuring reuse across regions, lines of business, and channels
- Partner with CEG product and business teams to translate questions into data problems and design clear analytic approaches
- Build and maintain dashboards and reports (e.g., contact volumes, handling time, quality, customer outcomes, agent performance, spot alerts) that provide reliable metrics and self-service access to data
- Perform deep-dive analysis to understand trends in contact patterns, customer issues, agent performance, and operational efficiency; identify root causes and propose practical, data-driven recommendations
- Define and maintain metrics and KPIs (e.g., CSAT, NPS, SLA, AHT, FCR, quality scores), ensuring consistent definitions across CEG teams and tools
- Communicate findings in simple, business-friendly language, including clear implications and recommended next steps
- Act as a trusted data partner for CEG stakeholders (operations leaders, product managers, WFM, QA, training, policy, and regional leadership)
- Work with other data engineers, analysts, and scientists to align on data standards, reusable components, and shared datasets for CEG
- Mentor junior and mid-level team members on data engineering, analytics, and software engineering best practices
- Contribute to continuous improvement of team workflows (code reviews, testing, documentation, runbooks, knowledge sharing, incident reviews)
- At Staff level, influence cross-team architecture and ways of working, making sure CEG’s data ecosystem scales with business growth
