Role Description You'll be part of a small, cross-functional data team that drives impact across Fabulous. We work closely with Product Growth, User Acquisition, and Finance. We take a full stack approach: every team member contributes across the full data spectrum. We don't specialize into Analytics Engineering or Data Science or Data Analysis. While we each have our own strengths, we contribute to projects across the entire spectrum of data work. This encourages strong ownership and drives impact. Practically, we work across three areas: - Analytics Engineering: Data modeling and transformations to build, maintain, and scale our analytics pipelines in dbt. Once an MVP is validated, we implement and improve it iteratively - with solid testing, data observability, and architecture that keeps tech debt manageable. - Applied Analytics: Data exploration, dashboard building in Omni, and ML model building. We aim for simplicity and interpretability but don't shy away from complexity when we face it. We work with highly data literate stakeholders - when building new models or dashboards we empower our users to continue to analyze the data themselves. - Data Science: ML model building to make predictions to inform the business quickly. Our stack: Fivetran · BigQuery · dbt · Amplitude · Omni This role is critical to the team's continued success: - Diverse, high-impact projects. You'll work on business-critical problems in close collaboration with business teams — improving metric accuracy, exploring new growth perspectives, building well-tested reporting pipelines, investigating data discrepancies, or applying statistics and ML where they add real value. - Building and maintaining our codebase. You'll contribute to solid analytics pipelines using SQL and dbt. Managing tech debt, improving engineering practices, and shaping the project's architecture are core to this role. - Growing into ownership. Over time, you'll own parts of the codebase, become the go-to person for at least one business team, and have a strong voice in how the analytics project evolves. - Leveling up the team. Share knowledge, and contribute to practices that help everyone grow. Speak up - we want you to shape the team's direction, cohesion, and mission. - Working autonomously. You'll manage your own projects and stakeholders. Clear, concise documentation is expected so future collaborators can build on your work. We embrace the age of AI. We use tools like Claude Code daily to write, review and ship code faster, while focusing our time on problem solving multiple projects in parallel. We are building out multiple entry points for our business stakeholders to interact with our data in natural language and believe this is the future of data work. You'll report to the Data Lead and be part of the Engineering team. Qualifications - University degree in Engineering, Computer Science, or Applied Mathematics - 4+ years of experience across data science, analytics, or analytics engineering - ideally with exposure to more than one of these - Excellent SQL skills, with experience building data models using dbt (or similar tools) - Strong engineering fundamentals: testing, clean code, peer review, CI/CD, git workflows - Passionate about the future of AI, researches the cutting edge, not afraid to experiment with new tools - Experience with cloud data warehouses and modern data stack tools - Self-starter who takes ownership, manages stakeholders, and communicates proactively - Excellent written and verbal English - Comfortable working remotely (we're remote-first) Requirements - Hands-on experience with Digital Marketing / User Acquisition (attribution, iOS privacy & SKAN, UA metrics) or Product / Growth (A/B testing, retention, monetization) - Familiarity with our stack (Amplitude, dbt, BigQuery, Omni) - Good analytical and statistical intuition - Prior experience in an agile startup environment Benefits - If you're excited about this role but don't check every box, we'd still love to hear from you. We value curiosity and growth mindset over a perfect resume.

• Conduct and deliver impactful data analyses to child welfare system leaders, including state directors, NGO executives, and program staff. • Prepare written reports, decks, and related documentation that translate analytical findings into actionable recommendations for program and policy improvement. • Conduct literature reviews, informational interviews, and exploratory data analysis to uncover causes of challenges in child welfare systems. • Transform complex datasets into analyzable formats using tools like R and Python. • Write transparent and maintainable code using version control systems like Git. • Apply statistical techniques to analyze large datasets and extract meaningful insights. • Develop and present data visualizations utilizing appropriate statistical packages. • Work closely with colleagues and external partners to share insights, present findings, and advance solutions. • Maintains technical and administrative support for a research project. • Analyzes and maintains data. • Conducts literature reviews. • Assists with preparation of reports, manuscripts and other documents.

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• Translate customer data into strategic intelligence to drive relationships, retention, and growth. • Serve as an analytical partner to CRM, Marketing, and Customer Intelligence teams. • Identify optimization opportunities and support data-driven decision making. • Directly contribute to improving the customer experience and increasing campaign effectiveness.