PushPress - Head of Data & AI Enablement

• Develop a clear, actionable data strategy — and own the process of getting buy-in across leadership, engineering, and product to execute it successfully • Architect and scale our data platform end-to-end — ingestion, transformation, storage, and serving — with a modern, layered approach (e.g., raw → cleaned → standardized metrics) that enables self-serve access across the company • Ensure data is synchronized, consistent, and reliable across all internal systems and tools (billing, CRM, product analytics, support, etc.) • Build and maintain the pipelines that feed our AI features, reporting products, and internal analytics • Establish data governance, quality standards, and observability practices as we scale • Make smart architecture and infrastructure decisions — knowing when to build, buy, or integrate — with a practical, data-informed perspective • Navigate the tension between building data capabilities for customer-facing products and investing in internal analytics and infrastructure — both are critical, and this role must keep them in balance • Understand the product development cycle well enough to anticipate data needs before they become blockers • Proactively plan data collection and instrumentation for upcoming product features, rather than reacting to requests after the fact • Ensure the right data is available, clean, and structured so engineering teams can build and ship AI features (AI Member Intel, AI Assistant, AI Reporting) confidently • Bring hands-on experience with LLMs and AI agents to help influence the company's evolving AI strategy — alongside engineering and product leadership • Stay close to the evolving AI landscape so you can be a credible thought partner to the teams building on top of your data platform • Own the data layer behind reporting features that gym owners use to understand their business — revenue trends, member engagement, class performance, churn signals, and more • Partner with product and design to ensure the underlying data is accurate, performant, and structured to support simple, actionable insights for non-technical users • Provide the data foundation that enables product and engineering to build differentiated, AI-powered experiences • Build the analytics foundation that enables the company to make faster, better decisions • Partner with go-to-market, product, and finance teams to deliver the data and reporting they need • Develop internal dashboards, self-serve tools, and reporting capabilities that scale with the company • Build and lead a high-performing data and AI team as the company grows • Set the technical direction, hiring plan, and team culture for the function • Foster a data-informed culture across the organization — make data accessible, understandable, and trusted • 8+ years of experience across data engineering, analytics, and AI, with at least 2 years in a senior leadership role • Deep hands-on experience building and scaling data platforms in a cloud environment (we're on AWS) • Strong IC foundation across multiple data disciplines — you've done the work yourself before leading others through it • A systems thinker who understands business context deeply, not just technical architecture • Strong understanding of modern data stack tooling — warehousing, ETL/ELT, orchestration, and BI • Familiarity with the AI/ML landscape — you don't need to train models from scratch, but you should know how to evaluate tools, APIs, and approaches and make smart infrastructure decisions that support AI-powered products • Track record of building and managing high-performing technical teams • Proven ability to develop a data strategy, build cross-functional buy-in, and execute it to measurable results — whether hands-on or by empowering your team • Ability to think strategically about where AI creates real value and where it doesn't • Comfortable operating in a fast-moving, resource-constrained environment where you'll need to prioritize ruthlessly • Excellent communicator who can translate technical concepts for non-technical stakeholders and serve as a bridge between business teams and data engineering • Hands-on experience with LLMs and AI agents in production systems • Background in data synchronization across multiple product surfaces and third-party integrations • Experience at a B2B SaaS company, ideally serving SMBs • Familiarity with vertical SaaS or fitness/wellness industry dynamics • Data science background — not necessarily ML-focused, but understands data transformation and modeling deeply