• Extract, transform, and analyze fleet‑scale manufacturing, testing, deployment, and operational data using advanced SQL (DML). • Understand and translate manufacturing, quality, and reliability concepts into measurable, data‑driven solutions and KPIs. • Perform statistical analysis on manufacturing test data to ensure data integrity, reliability, and readiness for automation and root‑cause analysis. • Continuously provide feedback upstream to improve data quality, coverage, and performance. • Identify and implement opportunities to automate manual workflows using AI and advanced analytics. • Develop AI‑driven recommendation systems for data standards, anomaly detection, intelligent data mapping, and quality insights. • Support the integration of AI and ML services (e.g., Vertex AI, LLMs) into analytics workflows and intelligent applications. • Partner with engineers and product teams to embed AI capabilities into hardware NPI and manufacturing processes. • Design, develop, and maintain interactive dashboards and reports using Looker Studio, Tableau, Power BI, or equivalent tools. • Visualize manufacturing performance, data quality scores, operational metrics, headcount status, and organizational spending. • Prepare executive‑level summaries and presentations that distill complex technical data into clear, actionable insights. • Provide leadership with real‑time, decision‑ready visibility into manufacturing and operational health. • Interact professionally with engineers, product owners, suppliers, and subject‑matter experts across manufacturing, quality, IT, and operations. • Provide technical guidance to suppliers and engineering teams on optimal data collection, standards, and data warehousing solutions. • Support multiple parallel initiatives by tracking progress, identifying risks, and escalating delivery impediments when needed. • Facilitate change management through documentation, communication plans, and process training.

• Extract, transform, and analyze fleet‑scale manufacturing, testing, deployment, and operational data using advanced SQL (DML). • Understand and translate manufacturing, quality, and reliability concepts into measurable, data‑driven solutions and KPIs. • Perform statistical analysis on manufacturing test data to ensure data integrity, reliability, and readiness for automation and root‑cause analysis. • Continuously provide feedback upstream to improve data quality, coverage, and performance. • Identify and implement opportunities to automate manual workflows using AI and advanced analytics. • Develop AI‑driven recommendation systems for data standards, anomaly detection, intelligent data mapping, and quality insights. • Support the integration of AI and ML services (e.g., Vertex AI, LLMs) into analytics workflows and intelligent applications. • Partner with engineers and product teams to embed AI capabilities into hardware NPI and manufacturing processes. • Design, develop, and maintain interactive dashboards and reports using Looker Studio, Tableau, Power BI, or equivalent tools. • Visualize manufacturing performance, data quality scores, operational metrics, headcount status, and organizational spending. • Prepare executive‑level summaries and presentations that distill complex technical data into clear, actionable insights. • Provide leadership with real‑time, decision‑ready visibility into manufacturing and operational health. • Interact professionally with engineers, product owners, suppliers, and subject‑matter experts across manufacturing, quality, IT, and operations. • Provide technical guidance to suppliers and engineering teams on optimal data collection, standards, and data warehousing solutions. • Support multiple parallel initiatives by tracking progress, identifying risks, and escalating delivery impediments when needed. • Facilitate change management through documentation, communication plans, and process training.

• Extract, transform, and analyze fleet‑scale manufacturing, testing, deployment, and operational data using advanced SQL (DML) • Understand and translate manufacturing, quality, and reliability concepts into measurable, data‑driven solutions and KPIs • Perform statistical analysis on manufacturing test data to ensure data integrity, reliability, and readiness for automation and root‑cause analysis • Continuously provide feedback upstream to improve data quality, coverage, and performance • Identify and implement opportunities to automate manual workflows using AI and advanced analytics • Develop AI‑driven recommendation systems for data standards, anomaly detection, intelligent data mapping, and quality insights • Support the integration of AI and ML services (e.g., Vertex AI, LLMs) into analytics workflows and intelligent applications • Partner with engineers and product teams to embed AI capabilities into hardware NPI and manufacturing processes • Design, develop, and maintain interactive dashboards and reports using Looker Studio, Tableau, Power BI, or equivalent tools • Visualize manufacturing performance, data quality scores, operational metrics, headcount status, and organizational spending • Prepare executive‑level summaries and presentations that distill complex technical data into clear, actionable insights • Provide leadership with real‑time, decision‑ready visibility into manufacturing and operational health • Interact professionally with engineers, product owners, suppliers, and subject‑matter experts across manufacturing, quality, IT, and operations • Provide technical guidance to suppliers and engineering teams on optimal data collection, standards, and data warehousing solutions • Support multiple parallel initiatives by tracking progress, identifying risks, and escalating delivery impediments when needed • Facilitate change management through documentation, communication plans, and process training.

• 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. • 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.