Lead Business Intelligence Analyst

Key Responsibilities

• Business Analysis & Stakeholder Partnership
  • Act as the primary analytics partner to senior commercial stakeholders across Sales, Marketing, Customer Success, and Revenue Operations.
  • Apply strong business analysis discipline — translating ambiguous business questions into well-scoped requirements, clear success criteria, and structured analytical briefs before any build begins.
  • Proactively surface insights, trends, risks, and opportunities that inform decision-making at leadership level.
  • Support strategic initiatives such as CRM consolidation, demand intelligence optimization, territory and book-of-business changes, and forecasting improvements.
  • Own end-to-end project management of analytics deliverables — from requirements gathering and prioritization through to delivery, adoption, and iteration — ensuring stakeholders are informed and timelines are met
• Communication, Enablement & Influence
  • Demonstrate exceptionally strong communication skills across all levels of the organisation — from crafting executive narratives for senior leadership forums to running hands-on enablement sessions for managers and frontline sales representatives.
  • Translate complex data, AI outputs, and analytical findings into plain-language stories that drive clarity, confidence, and action.
  • Design and deliver enablement programmes that help managers and reps understand, trust, and act on dashboards, KPIs, and AI-generated insights — building genuine data literacy across the Revenue Organisation rather than dependency on the BI team.
  • Champion clear, consistent communication of what metrics mean, how they are calculated, and why they matter — removing ambiguity at every level.
  • Influence without authority; build credibility with senior stakeholders through the quality of insight and the integrity of the underlying data.

• Analytics, Reporting & Insight
  • Own the design and delivery of executive-level dashboards and self-service reporting, primarily using Power BI.
  • Ensure consistent definitions and calculations for revenue KPIs, North Star metrics, and commercial performance indicators.
  • Conduct deep-dive analyses to diagnose performance issues and recommend actionable next steps.
  • Write and optimise advanced SQL queries to extract, transform, and validate data across our Databricks data warehouse and connected source systems.
  • Leverage Databricks as the central analytical platform — working within notebooks, SQL warehouses, and the broader Lakehouse environment to build reliable, scalable analytical outputs.

• AI & LLM Integration
  • Be a power user and internal advocate of LLM-based tools, including Microsoft Copilot and Anthropic Claude (including Claude Code), using them to accelerate analysis, generate and review SQL, draft communications, and surface insight more efficiently.
  • Help define and create LLM agents for the Go To Market organisation – connecting context to frontline AI tools such as MS Copilot and Salesloft.
  • Proactively identify opportunities to embed LLM and AI capabilities into BI workflows, dashboards, and analytical processes — raising the ceiling of what the team can deliver.
  • Work with the Senior Manager of BI to evaluate and scale AI-assisted analytics approaches across the team, including the use of Databricks' native LLM and AI capabilities.
  • Help commercial stakeholders and frontline teams understand and appropriately trust AI-generated outputs — building confidence in AI tools through clear explanation, demonstration, and enablement.

• Data Modelling & Quality
  • Partner with Data Analytics Engineers to define analytical data models that support revenue, pipeline, customer lifecycle, and performance reporting.
  • Validate data accuracy and integrity across the analytics layer, proactively identifying and resolving issues before they impact decision-making.
  • Contribute to data governance standards, metric documentation, and reporting best practices.
• Technology & Tooling Context
  • The Lead Business Analyst will work within a modern BI and revenue intelligence stack, including:
  • Layer Tools
  • Data Warehouse & Analytics Databricks (central data warehouse with LLM capabilities)
  • BI & Visualisation Power BI
  • AI & LLM Tooling Microsoft Copilot, Anthropic Claude, Claude Code, Databrick Genie, Databricks hosted LLM models
  • Core Commercial Systems Salesforce (multiple instance), PlanHat, &
  • Revenue & Demand
  • Intelligence Clay
  • Sales Enablement &
  • Engagement Salesloft (including AI and conversational intelligence) & HighSpot
  • A strong understanding of how these systems connect — and how data flows end-to-end from source systems to executive dashboards and frontline tools — is essential.
• Required Experience & Skills

• • 8+ years of experience in a Business Analysis, Analytics, or BI within a SaaS or technology-driven organisation.
  • Experience supporting RevOps, Sales Operations, or Commercial Analytics teams.
  • Advanced SQL skills — comfortable writing complex queries, debugging data issues, and working directly within a cloud data warehouse environment; Databricks experience strongly preferred.
  • Strong business analysis capability — proven ability to scope, structure, and document analytical requirements with rigour before moving into delivery.
  • Proven project management skills — able to manage multiple concurrent workstreams, set and meet deadlines, and maintain clear stakeholder communication throughout.
  • Exceptionally strong communication skills — a natural and compelling communicator in written and verbal form, equally effective presenting strategy to a VP as running a training session for a team of account executives.
  • Demonstrated experience designing and delivering stakeholder enablement on data, dashboards, or analytics tooling — not just building outputs, but ensuring they are understood and adopted.
  • Proven experience delivering executive-ready dashboards and self-service reporting (Power BI preferred).
  • Strong commercial and revenue-focused business acumen.
  • Active and confident user of LLM tools such as Microsoft Copilot or Anthropic Claude in a professional analytical context.
• Preferred Experience

• • Hands-on experience with Databricks — including SQL warehouses, notebooks, and the Lakehouse architecture.
  • Experience using Claude Code or similar AI coding assistants to accelerate SQL development, data validation, or analytical scripting.
  • Familiarity with CRM and GTM analytics — Salesforce, pipeline reporting, forecasting, MEDDPICC, territory models.
  • Hands-on Experience with Clay
  • Ideally experience with Python (e.g. connecting to APIs)
  • Exposure to building or scaling data literacy programmes within a commercial organisation.
• What Success Looks Like

• Leaders trust the dashboards and insights/recommendations produced and use them to run the business with confidence.
• Managers and frontline reps understand and act on data, insights, and AI outputs — because this role has invested in enabling them, not just building for them.
• KPIs and metrics are clearly defined, consistently applied, and widely adopted across the organisation.
• LLM and AI tools are meaningfully embedded into BI workflows, improving speed, quality, and reach of analytical output.
• The BI team is seen as a strategic partner — not just a reporting function.
• Data quality issues are proactively identified and resolved before impacting decision-making.
• Projects are delivered on time, with clear communication and minimal surprises for stakeholders.