Job Responsibilities
Build, publish, and maintain intuitive, high-performance dashboards and reports (primarily using Power BI or equivalent) to track operational metrics
Establish and maintain robust customer SLA reporting templates and structures that empower non-technical business leaders to easily perform self-service data exploration
Collaborate with data intelligence teams and enable structuring of raw datasets into clean, reporting-ready datasets.
Standardize, define, and document enterprise-level KPIs and metrics to ensure a single version of the truth across all business units.
Establish and maintain a centralized KPI repository and nomenclature in alignment with customer SLAs across all service tiers.
Perform deep-dive analyses including trend analysis, root-cause identification, and hypothesis testing to solve critical business bottlenecks.
Develop predictive models and deploy basic data science pipelines (Python/R) to forecast operational performance and customer success metrics.
Partner directly with global operations teams to translate complex or region specific business problems into highly structured analytical use cases.
Bridge the Gap: Serve as the technical translator between data Infrastructure Engineers, Data Scientists, and Operations, Finance, and other business stakeholders.
Communicate technical findings through compelling visual storytelling and actionable data presentations to non-technical business leaders.
Enforce data governance and resolve data quality anomalies in coordination with data engineering team.
