• Take full ownership of local IT operations across Europe, AMWA, and APAC. • Consolidate what is currently a patchwork of country-level teams, vendors, and operating models into one coherent, centrally governed operations organisation with clear accountability and consistent standards. • Build and form regional IT operations leadership teams: recruit, develop, and empower strong regional leads who can run day-to-day operations with autonomy while delivering against global standards. • Run local onsite support and field engineering across offices, plants, quarries, and production sites. • Deliver against SLAs that meet country expectations. • Restructure regional operations to drive savings: identify overlaps, eliminate duplication, optimise team sizes, renegotiate vendor contracts, and design smarter support models—while maintaining or improving service quality. • Own vendor management and third-party relationships for regional IT operations: manage outsourcing partners, negotiate contracts, enforce SLAs, and ensure that external providers deliver the same quality and responsiveness as internal teams. • Establish a unified incident management and escalation framework across all regions. • Collaborate with the global Infrastructure team to drive infrastructure standardisation across sites.

• Researches and resolves high complexity collection account balances according to DaVita policies and procedures • Provides ongoing training and guidance for Revenue Specialist teammates • Performs data trending analysis, quality assurance reviews and review of key metrics • Review weekly and monthly key metrics to identify trends or areas of focus • Work with Management to develop, document, and implement action plans to address issues • Identify payer trends and or root cause of billing or claim exceptions; take appropriate steps to resolve and/or escalate issues • Act as resource for teammate’s questions and assist with issues of focus and problematic payer issues • Train new teammates and identify training opportunities to improve individual and team performance

• Build dashboards and recurring reporting from scratch, using visualization tools (such as Tableau, PowerBI, Excel, etc..) deciding what belongs on them, what doesn't, and why • Pull and reshape data exported from CRMs, help desks, POS systems, and spreadsheets into reporting that answers a real business question • Dig through sales, operational, and cost data to find anomalies, trends, and areas for improvement — and frame what you find so a decision-maker can act on it • Report on things like sales performance, lead funnels, yield, response times, and cost trends across client accounts • Read a P&L or income statement well enough to know when something looks off, even if you didn't build it • Partner with our delivery and client teams to define what's worth measuring in the first place**

• Available to Lead and govern the end-to-end incident management lifecycle, including detection, triage, escalation, and resolution. • Drive major incident management (MIM) processes and communications. • Improve MTTR (Mean Time to Resolution) through automation and process optimization. • Establish and maintain incident response playbooks and runbooks. • Maintain and improve intelligent heatmaps leveraging AI/ML to identify recurring technical themes and prioritize long-term remediation. • Implement trend analysis and proactive problem identification using observability data and AI. • Track and manage problem records to closure. • Govern change management processes (lead the CAB), ensuring safe, compliant, and low-risk deployments. • Define and enforce change policies, risk assessments, and approval workflows. • Drive continuous improvement in release and deployment practices. • Maintain a strong understanding of system architecture and monitoring strategies, identifying gaps and opportunities for improvement. • Partner with engineering teams to improve system resilience and performance. • Reduce alert fatigue by improving signal-to-noise ratio in monitoring systems. • Leverage AI/ML for anomaly detection, predictive alerting, and automated root cause analysis. • Implement AI-driven solutions to optimize incident response and operational workflows. • Analyze large-scale operational data to identify patterns and recommend improvements.