Data Analyst; Remote

The Data Analyst collaborates with key organizational leaders, including the C-suite, to deliver cohesive, rigorous, and insightful reporting tools and in-depth analyses that facilitate data-driven decision making. This role translates complex data into clear, actionable insights that align with strategic business goals, while collaborating across technical and non-technical teams to foster a culture of data literacy and democratization. The role serves as a trusted thought-partner and problem-solver, bridging the gap between data and strategy to support impactful business outcomes.

• Works collaboratively with internal teams to solve cross-functional business challenges and provide key insights that drive improvement for both the business and its customers.
• Translates analyses into actionable, data-backed recommendations and summaries of expected results.
• Analyzes operational initiatives to measure their effectiveness and impact on key performance indicators (KPIs), enables in-depth understanding of existing performance, and recommends process optimizations based on a data-driven analysis of expected results
• Conducts deep-dive root cause analyses (RCA) to determine key drivers of performance, observed changes, and trends.
• Develops, refines, and leverages cross-functional dashboards and reports to provide timely data for decision-making, as well as regular performance updates to stakeholders.
• Collects, integrates, and interprets cross-functional and disparate data sets to provide holistic insights, recommendations, and analysis of business value impact across the acquisition funnel, including marketing, sales, and operations.
• Prototypes and develops PoC tools to enable deeper organizational understanding of performance, year-over-year pacing, and progress against goals.
• Leverages advanced analytics modeling, including regression, forecasting, segmentation, and similar methodologies, to deliver significant and rigorous business insights.
• Supports A/B and experimental test design; leads analysis of test data and delivery of actionable results using rigorous statistical analysis methodologies
• Maintains a detailed understanding of platforms, data structures, and data architecture to serve as a reliable organizational resource and contribute to the evolution of analytics capabilities and frameworks.
• Partners closely with Data Engineering and Reporting teams to communicate and collaborate on complex data needs and projects supporting business requirements, automation initiatives, and metric alignment.
• Independently determines and pursues deep-dive opportunities for ad hoc analysis and predictive analytics to provide insights and recommendations to key business leaders.
• Serves as a key partner with internal team members to refine and optimize organizational performance efforts.
• Develops calculation models, wireframes, and requirements for Data Engineering and Reporting developers in support of initiatives related to the automation of data pipelines and automated, dynamic reporting tools.
• Maintains data dictionaries, metric definitions, event mapping, and documentation of data sources.
• Ensures data security and compliance with data privacy regulations.
• Performs other duties as assigned.

• A bachelor’s degree in business, economics, statistics, analytics or a related field is required. Equivalent combinations of education and relevant experience may be considered.
• A minimum of five (5) years in a role with demonstrated analytical background and expertise (e.g., data analysis, business analytics, statistical analysis, forecasting/demand management).
• Proficiency in data analysis tools and languages such as SQL, DAX, Python, and/or R with advanced skill in Excel.
• Strong data visualization skills using Power BI and/or Tableau as well as for storytelling in presentations of results to organizational leadership.
• Excellent analytical and statistical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
• Solid understanding of statistical concepts and data analysis techniques, including evaluation of ROI and attribution, hypothesis testing, confidence intervals, and forecasting methodology.
• Demonstrated proficiency with a broad range of technologies and tools related to data visualization, operational platforms, Microsoft Fabric and/or Azure data environments, Git repositories, Salesforce data, GA4, and the analysis of…