• Act as a trusted business advisor to the Core Service team, proactively identifying trends and driving efficiency improvements • Own the design of dashboards, metrics, and reports, turning data into actionable insights for the fraud, funding, risk and support experience teams • Dive into large, complex datasets/logs to turn your findings into scalable data models necessary to facilitate reporting, analytics and ML models • Lead cross-functional projects with analysts, scientists, engineers, and product teams to build data pipelines using Airflow, Python, and DBT • Develop and automate reporting of key performance indicators across core services teams • Be at the forefront of data innovation by experimenting, testing hypotheses, and translating insights into action. • Develop high-quality code for pipelines and dashboards, incorporating software engineering best practices

• Analyze real-time telemetry alerts and operational datasets to validate incidents, identify anomalies, and assess operational impact. • Differentiate true events from false positives, duplicate alerts, and known recurring conditions. • Correlate trends, timestamps, logs, and event data to support severity assessment and operational decision-making. • Prepare clear, data-backed incident summaries to support client and stakeholder response activities. • Provide analytical context and data-driven insights to clients and external vendors during incident investigations. • Track remediation actions, response timelines, and outcomes using structured operational data. • Support escalation and prioritization decisions through evidence-based analysis. Communicate findings clearly and concisely to operations, service management, and project stakeholders. • Identify recurring incident patterns, systemic operational risks, and vendor performance trends. • Highlight gaps in telemetry coverage, data quality, alert logic, or monitoring effectiveness. • Recommend opportunities for alert tuning, automation, and improved monitoring or reporting practices. • Support continuous improvement initiatives by surfacing actionable insights from operational data. • Maintain accurate, auditable records of analyses, incidents, and findings in approved client and company tools. • Contribute analytical insights to improve runbooks, alert definitions, and operational standards. • Support initiatives aimed at reducing Mean Time to Resolution (MTTR) and minimizing repeat incidents.

• Lead and mentor a team of analysts, ensuring they have the support and clarity needed to succeed in a fast-paced environment. • Understand funnels, ecosystems, user behaviors, and long-term trends in the adoption of our products to identify opportunities for step-changes of specific customer segments. • Develop and maintain comprehensive KPI trees and frameworks to provide clear visibility on domain performance and trends. • Define key metrics reflecting product success and monitor health, while identifying growth opportunities in retention, engagement, and monetization through quantitative analysis. • Partner closely with Product Managers, Engineers, and senior stakeholders to translate data insights into actionable narratives and strategies. • Lead efforts to automate data management and reporting processes, collaborating with Business Intelligence and Data Engineering teams. • Champion a hypothesis-driven approach, overseeing product A/B testing and funnel optimization to de-risk development.

• Support the execution and optimization of existing customer and remarketing retention marketing strategies through data analysis, reporting, measurement, and testing. • Collaborate across Marketing, Risk, Product, and Data Science teams to enhance retention strategies while ensuring compliance and data quality. • Analyze and optimize marketing campaigns across Email, SMS, Push, and Authenticated channels. • Conduct audits of marketing data for accuracy, compliance, and consistency with credit policy. • Develop, optimize, and maintain data and reporting solutions, including SQL tables, Power BI dashboards, and automated reporting views. • Perform deep-dive analysis on large datasets to uncover insights that improve targeting, messaging, and channel performance.