Everway - Director, Analytics & Decision Intelligence

• 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. • Map and prioritise the organisation's critical decisions. • 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. • Proactively surface what the business hasn't seen. • 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. • Lead the analytics function as a product team • 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. • Design and deliver gold-tier data products • 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. • Architect and own the self-service analytics model • 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. • Own the semantic layer strategy • 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. • Partner with data engineering on data contracts and gold layer design • 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. • Own the BI platform end-to-end. • 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. • Lead the adoption of AI-powered analytics • 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. • Drive data literacy and decision-readiness across the organisation • 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. • Establish and maintain catalogue presence for all analytics assets • Define and enforce analytics engineering standards — including dashboard design patterns, naming conventions, version control, testing protocols, and peer review processes. • Define and enforce analytics engineering standards • 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. • Measure what matters. • Mentor and develop analysts within the team — building technical depth, product thinking, decision-framing skills, and a culture of ownership and quality. • Mentor and develop analysts within the team • Essential Criteria • 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. • Demonstrated ability to work backwards from business decisions to analytics requirements • 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. • A track record of proactive insight discovery • 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 • 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.