Director, Analytics & Decision Intelligence

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 and encouraged to show up as they are, with different ways of thinking welcomed and valued. We pride ourselves on our core values that are embedded within our culture. These are to be curious, have courage, and commit fully.

Join us at Everway - together, we can unlock the full potential of every mind.

About the role

Every function at Everway — sales, finance, customer success, product — makes decisions every day that shape revenue, retention, and learner outcomes. Too many of those decisions are made without the data confidence they deserve. This role exists to change that.

We're looking for someone to lead our analytics function with a decision-first mindset — someone who starts by understanding the decisions the business needs to make, then works backwards to design the data products, self-service experiences, and insight surfaces that make those decisions faster, more confident, and more consistent.

You'll be building and leading a product-oriented analytics team that designs gold-tier data products with clear ownership, documented contracts, defined SLAs, and measurable impact on the decisions they serve. Outputs might be interactive dashboards, governed self-service datasets published from our Databricks lakehouse, embedded metrics, or AI-assisted exploration — but in every case, the measure of success is the same: did the decision get better?

Reporting to the VP of Data, you'll work closely with stakeholders across the business to map critical decision points and uncover the ones the business hasn't identified yet — surfacing risks, patterns, and opportunities that would otherwise go unseen. You'll partner with data engineering on data contracts and gold layer design, and play a hands-on role in shaping how we adopt AI-powered analytics — from natural language interfaces to LLM-assisted workflows — always within a governed framework.

Responsibilities

• Map and prioritise the organisation's critical decisions. Work across product, sales, finance, customer success, and leadership to identify the highest-impact decisions in each domain, understand how they're currently made, and define what data — at what quality, freshness, and granularity — would materially improve them.
• Proactively surface what the business hasn't seen. Go beyond answering known questions — use exploratory analysis, anomaly detection, and cross-domain pattern recognition to identify risks, opportunities, and emerging trends that stakeholders haven't asked about yet. The best analytics functions don't just support decisions — they trigger them.
• Lead the analytics function as a product team — setting the vision, owning the roadmap, and establishing the operating model. The backlog is organised around decisions to be supported, not requests to be fulfilled.
• Design and deliver gold-tier data products with clear ownership, versioning, documented data contracts, and defined SLAs — treating every analytics output as a product with a lifecycle, users, and success metrics tied to decision outcomes.
• Architect and own the self-service analytics model — defining tiered access (raw, curated, pre-built), designing governed exploration spaces backed by certified Databricks-published datasets, and enabling domain teams to answer their own recurring decision questions without waiting for the analytics team.
• Own the semantic layer strategy — defining where metric logic lives (dbt/MetricFlow, BI published data sources, or a dedicated semantic layer tool) and ensuring KPI definitions are consistent, governed, and trustworthy across every consumption surface.
• Partner with data engineering on data contracts and gold layer design — defining consumption requirements upstream so that the medallion architecture is built with decision use cases in mind, not retrofitted to them.
• Own the BI platform end-to-end. Our current environment is Tableau — you'll own certified content standards, performance optimisation, publishing governance, Unity Catalog integration, and access management. You'll also be expected to validate and evolve our BI tooling strategy as the platform matures, not simply inherit it.
• Lead the adoption of AI-powered analytics — including automated insight delivery, natural language query interfaces, and LLM-assisted workflows for documentation, anomaly surfacing, and exploratory analysis. Develop a clear point of view on where governed data products outperform AI-generated answers, and where the two complement each other.
• Drive data literacy and decision-readiness across the organisation — moving teams from dependence on the analytics function to confident, independent data consumers through enablement, documentation, and curated discovery experiences.
• Establish and maintain catalogue presence for all analytics assets — ensuring certified datasets, dashboards, and metrics are discoverable, documented, and accompanied by trust signals including lineage, freshness, and ownership.
• Define and enforce analytics engineering standards — including dashboard design patterns, naming conventions, version control, testing protocols, and peer review processes.
• Measure what matters. Continuously assess whether analytics products are actually improving the decisions they were designed to support — using adoption metrics, stakeholder feedback, decision cycle time, and platform usage data to prioritise the roadmap and retire low-value assets.
• Mentor and develop analysts within the team — building technical depth, product thinking, decision-framing skills, and a culture of ownership and quality.

Essential Criteria

• 3+ years in business intelligence, analytics, or a decision science role, with at least 2 years leading a BI or analytics function in a senior capacity.
• Demonstrated ability to work backwards from business decisions to analytics requirements — you can sit with a commercial leader, understand the decisions they face, and translate that into a scoped analytics product with defined users, data requirements, and success metrics. This is the core skill.
• A track record of proactive insight discovery — you don't wait to be asked. You've identified risks, opportunities, or behavioural patterns that the business didn't know to look for, and translated them into action.
• Expert-level proficiency in at least one enterprise BI platform (Tableau, Power BI, Looker, or equivalent) — including complex calculations, performance optimisation, governed content management, and platform administration. You have a clear point of view on what good looks like and can enforce it across a team.
• Strong SQL skills with the ability to write, optimise, and review complex queries against large datasets in a cloud data platform (Databricks, Snowflake, BigQuery, or equivalent).
• Demonstrable experience delivering BI outputs as data products — with defined ownership, documented contracts, versioning, SLAs, and lifecycle management.
• A proven track record of designing and scaling self-service analytics — including tiered access models, governed exploration, certified datasets, and the enablement work that makes self-service actually stick.
• Deep expertise in semantic layer design and metrics governance — with a considered view on where metric logic should live and experience implementing that at scale.
• Strong understanding of lakehouse and medallion architecture — how data contracts between engineering and analytics work in practice, and how upstream modelling decisions affect downstream quality and performance.
• Experience working within a governed data platform — including cataloguing, lineage, certified asset management, and trust signals.
• Excellent stakeholder management and communication — you present clearly to audiences from data engineers to the C-suite and are comfortable pushing back when a request is the wrong question.
• A track record of building and leading high-performing analytics teams — hiring, mentoring, setting standards, and creating a culture of ownership and product thinking.
• Strong commercial acumen — you understand SaaS metrics, the decisions they inform, and how analytics can directly improve decision quality across the business.

Desirable Criteria

• Hands-on experience with Databricks — including Delta tables, Unity Catalog, and lakehouse-native consumption patterns. (Candidates with this experience will be strongly preferred.)
• Hands-on experience with Tableau — including Tableau Cloud/Server administration, LOD expressions, certified data source governance, and Pulse. (Our current BI platform; candidates with this experience will be strongly preferred.)
• Practical experience with dbt — including transformation models, MetricFlow or semantic layer definitions, and dbt docs for governed discoverability.
• Experience with decision intelligence, decision science, or analytics frameworks that explicitly link data products to the decisions they support.
• Hands-on experience with AI-powered BI capabilities (Tableau Pulse, Databricks Genie, or similar) and a view on where these complement vs. replace governed products.
• Experience using LLMs or generative AI to enhance analytics workflows — documentation automation, natural language interfaces, or exploratory analysis acceleration.
• Background in SaaS, with fluency in ARR, NRR, churn, CAC, and product adoption metrics.
• Experience with data quality frameworks and observability tooling (dbt tests, Great Expectations, Monte Carlo, or similar).
• Familiarity with Python for analytical automation or light statistical work.
• Comfort with agile delivery, product backlogs, and iterating based on stakeholder feedback.

 

Please submit your application on out website by Wednesday 10th June 2026.

Please note: applications may close early due to high demand, so early submission is encouraged.

Join our team and enjoy a competitive salary with bonus opportunities, flexible work schedules, and comprehensive health and wellness benefits. We offer flexible time off plans, career growth through development programs, and a collaborative, innovative culture where your ideas matter.  Ready to make an impact? Apply today and be part of a company that invests in your success!

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