Data Analyst (Product Analytics Focus with Cross-Functional Collaboration)

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Requirements:

• Advanced SQL skills for querying complex datasets, optimizing performance, and building custom data models.
• Proficiency in Python for data manipulation, statistical analysis, automation, and developing quick ETL workflows.
• Experience with product analytics tools such as Google Analytics, Amplitude, Mixpanel, or similar platforms.
• Familiarity with UI/UX analytics, including heatmaps, clickstream analysis, and user flow tracking.
• Expertise in creating dashboards and reports using Tableau, Power BI, or other visualization tools.
• Strong foundation in statistical analysis, A/B testing, and experimentation methodologies.
• Experience with cohort analysis, retention analysis, and growth metrics to support business growth initiatives.
• Proven ability to work effectively with diverse stakeholders across Product, Marketing, Finance, Operations, and Donor Development.

Responsibilities:

• Analyze user behavior, feature adoption, retention trends, and app engagement to identify opportunities for product improvement.
• Conduct cohort analyses, funnel tracking, and A/B test evaluations to measure the impact of product changes and experiments.
• Partner with Product Managers to translate business questions into analytical solutions, providing data-driven recommendations to influence product strategy.
• Collaborate with teams across Marketing, Finance, Operations, Donor Development, and other departments to understand their data needs and deliver actionable insights.
• Support marketing initiatives with campaign performance analysis, segmentation strategies, and conversion tracking.
• Provide financial analysis support related to donation trends, revenue forecasts, and operational efficiency metrics.
• Contribute to donor analytics by tracking engagement, retention, and giving patterns to optimize fundraising strategies.
• Write complex SQL queries to extract, manipulate, and analyze large datasets from Snowflake, BigQuery, and other data sources.
• Use Python for data wrangling, statistical analysis, automation of repetitive tasks, and developing quick ETL pipelines as needed.
• Independently identify and implement solutions to data challenges, minimizing dependencies on Data Engineers.
• Design and build interactive dashboards in Tableau, Power BI, or custom solutions to visualize key KPIs such as MAUs, donation trends, campaign performance, and operational metrics.
• Automate reporting processes to improve efficiency, reduce manual errors, and ensure consistent access to up-to-date data.
• Continuously improve dashboards based on user feedback and evolving business needs, ensuring insights are both accessible and impactful.
• Leverage product analytics tools (e.g., Google Analytics, Amplitude, Mixpanel) to analyze user interactions and behaviors within digital products.
• Conduct heatmap analyses, clickstream tracking, and user journey mapping to identify friction points and areas for optimization.
• Provide data-driven recommendations to Product and Design teams to enhance user experiences and improve conversion rates.
• Design, implement, and analyze A/B tests to evaluate new product features, marketing campaigns, and operational changes.
• Develop statistical models to forecast user growth, donor retention, and revenue trends based on historical data and predictive analytics.
• Recommend growth strategies backed by data-driven experimentation and robust analysis.
• Ensure data accuracy, consistency, and reliability across all reports and analyses by implementing data validation checks and monitoring data pipelines.
• Partner with the Data Architect and Data Governance Lead to uphold data standards and best practices across the organization.
• Proactively identify and resolve data discrepancies, ensuring high data integrity in all outputs.
• Stay current on emerging trends in product analytics, data science, and business intelligence tools.
• Experiment with new technologies, programming languages, and analytical methods to continuously enhance data capabilities.
• Share knowledge and best practices with the broader ACoE team, contributing to a culture of continuous improvement and data literacy across the organization.