Role Description Celigo is looking for a Data Analyst to serve as the analytical engine behind smarter, faster decision-making across the business. You will own the full lifecycle of insight delivery — from translating ambiguous business questions into structured frameworks, to building the dashboards and self-serve reporting that give stakeholders real-time visibility into business performance. - Own and develop dashboards, reports, and self-serve analytics that give stakeholders timely, accurate visibility into business performance. - Conduct deep-dive analyses across key business areas — including revenue, product usage, customer behavior, and operational efficiency — to surface actionable insights. - Partner with Analytics/Data Engineering to define metrics, validate data quality, and ensure analytical outputs are built on a reliable, well-modeled foundation. - Help establish consistent metric definitions, reporting standards, and documentation practices that can scale as the analytics function grows. - Translate ambiguous business questions into structured analytical frameworks, and communicate findings clearly to both technical and non-technical audiences. - Leverage AI-assisted tools to accelerate analysis, automate recurring reporting workflows, and improve the efficiency of the analytics development cycle. - Proactively identify trends, anomalies, and opportunities in the data and bring them forward without waiting to be asked. - Collaborate cross-functionally to support data-driven decision-making across the organization. Qualifications - Strong analytical instincts — comfortable forming hypotheses, stress-testing assumptions, and telling a clear story with data. - Highly proficient in SQL, with the ability to write complex queries across large datasets in a cloud data warehouse environment (Snowflake preferred). - Experience with BI tools such as Sigma, Looker, Tableau, Power BI, or similar for building and maintaining dashboards and self-serve reporting. - Familiar with using AI tools (e.g., Claude, ChatGPT, Copilot, or similar) to accelerate analysis, generate code, and automate repetitive workflows. - Excellent communication skills — able to translate data findings into concise, actionable narratives for business stakeholders at all levels. - Self-starter who thrives in unstructured environments and takes initiative to define processes where none yet exist. Requirements - Degree in Data Analytics, Statistics, Economics, Business, Computer Science, or a related field. - 2–5 years of experience in a data analyst or business intelligence role, preferably in a fast-paced SaaS environment. - Advanced proficiency in SQL; experience with Python or R for analysis is a plus. - Hands-on experience with Snowflake or a comparable cloud data warehouse. - Demonstrated ability to build and maintain dashboards and reporting frameworks used by business stakeholders. - Experience working with go-to-market, finance, or product data is a strong plus. - Familiarity with DBT or similar data modeling tools is a bonus. Benefits - Three weeks of vacation (starting year one) - Wellness days and holidays to recharge - Parental leave and a generous benefits package - Monthly tech stipend - Recognition and career development opportunities

• Develop and maintain scalable, resilient data pipelines (ETL/ELT). • Design and implement data architectures in cloud and/or on-premises environments. • Integrate data via APIs, web services, and automated processes. • Build and maintain data collection solutions (crawlers, scrapers, batch and streaming ingestion). • Ensure data quality, standardization, and governance. • Monitor and optimize the performance of data pipelines and data processes. • Support business and technology teams with data-driven solutions. • Document processes, workflows, and architectures following best practices. • Contribute to the continuous evolution of data solutions and tools.

• Lead the design, development, and implementation of advanced machine learning models and algorithms to solve complex business problems. • Collaborate with cross-functional teams, including product managers, engineers, and business stakeholders, to identify data-driven opportunities and translate them into actionable insights. • Analyze large datasets to extract meaningful patterns, trends, and relationships, leveraging statistical methods, machine learning techniques, and data visualization tools. • Develop and validate approaches, ensuring accuracy, robustness, and scalability for deployment in production environments. • Communicate findings and recommendations to both technical and non-technical audiences through detailed reports, presentations, and data visualizations. • Mentor and provide guidance to junior data scientists and analysts, fostering a culture of continuous learning and innovation.

• Lead portfolio performance and risk-trend analyses, incorporating macroeconomic context and translating results into business recommendations. • Provide insights beyond core datasets by triangulating quantitative findings with customer behavior, operational signals, and market dynamics. • Lead risk-based pricing strategy and model development to optimize expected loss, yield, and unit economics across acquisitions and originations. • Monitor pricing performance and drive tests/guardrails to improve outcomes. • Set strategy and KPIs to improve funder, underwriting, and servicing effectiveness across key credit dimensions. • Drive initiatives to reduce delinquency, losses, and roll-rates with prioritized action plans. • Design and measure collection and servicing strategies, standardize reporting and data quality monitoring. • Lead a team of data scientists and deliver scalable, AI-ready datasets. • Partner with Risk on underwriting and pricing, monitoring model performance, and driving improvements. • Partner with Sales to provide analytics support for program enhancements.