Senior Analytics Product Lead

Every mind is unique. Yet much of the world is still built for what’s considered "normal," leaving too many people behind. At Everway, we change that by creating technology that helps everyone understand and be understood. By understanding and addressing the unique needs of each individual, we're creating a world where differences are recognized and valued.

Our careers fit real life. When you join us, you’re not just taking a job; you’re joining a movement to build a more neuroinclusive world. We’re a global community of over 800 employees spanning North America, UK, Europe, Australia, and New Zealand. A career here is purposeful and fast-moving, with clear expectations, modern tools, and the clarity to focus on what matters most.

Our people are supported to show up as themselves, with different ways of thinking welcomed and valued. We pride ourselves on our core values: to be curious, have courage, and commit fully.

About the Role

Every function at Everway — sales, finance, customer success, product — makes decisions daily that shape revenue, retention, and learner outcomes. Yet too many of these decisions lack the data confidence they deserve. This role aims to change that.

We seek someone to lead our analytics function with a decision-first mindset—someone who understands the decisions the business needs to make and designs data products, self-service experiences, and insight surfaces to make those decisions faster, more confident, and more consistent.

You’ll be heading an analytics product team, designing and delivering gold-tier data products that embed clear ownership, documented contracts, defined SLAs, and measurable impact on decision outcomes. The outputs could be interactive dashboards, governed self-service datasets, embedded metrics, or AI-assisted exploration—but the focus remains constant: do the decisions get better?

Reporting to the VP of Data, you’ll collaborate closely with stakeholders across the business to map critical decision points—identifying and surfacing risks, patterns, and opportunities often unseen. You’ll shape data contracts, govern gold layer design, and lead adoption of AI-powered analytics while maintaining a governed framework.

Responsibilities

• Map and prioritise the organisation's critical decisions, working with stakeholders across product, sales, finance, customer success, and leadership to:
  • Identify the highest-impact decisions in each domain.
  • Understand current decision-making processes.
  • Define high-quality, timely, and granular data to improve decision-making.
• Proactively surface insights the business hasn’t yet identified, leveraging:
  • Exploratory analysis.
  • Anomaly detection.
  • Cross-domain pattern recognition to reveal risks and opportunities.
• Act as a product-oriented leader, defining the vision and roadmap for the analytics function to support decision outcomes, not requests.
• Design and deliver gold-tier data products with:
  • Clear ownership, versioning, and data contracts.
  • Versioning, SLAs, and a defined lifecycle—treating every output as a product with measurable impact.
• Architect a governed self-service model, including:
  • Tiered data access (raw, curated, pre-built).
  • Governed discovery spaces with certified Databricks datasets.
• Establish semantic layer strategy by:
  • Defining KPI locations (dbt/MetricFlow or dedicated tools).
  • Ensuring trustworthy, consistent definitions across all surfaces.
• Partner with data engineering to align data contracts and design decision-focused architectures.
• Lead end-to-end BI platform management, currently Tableau, by:
  • Enforcing certified content standards and performance optimisation.
  • Validating strategy, governance, and Unity Catalog integration.
• Drive adoption of AI-powered analytics, including:
  • Automated insights and natural language interfaces.
  • Workflows for documentation and LLM-assisted analysis.
  • Balancing AI with governed data products and decision needs.
• Foster data literacy to restore independence and confidence across teams.
• Maintain an analytics asset catalogue for discoverability, documentation, and trust signals (lineage, freshness, ownership).
• Define engineering standards for testing, version control, and governance in analytics outputs.
• Continuously assess impact by tracking measurement metrics like adoption, feedback cycles, and decision efficacy.
• Mentor and develop analysts within the team on product thinking, decision frameworks, and ownership culture[.]