[Hiring] Lead Business Intelligence Analyst @Cision

Role Description

We are seeking a Lead Business Analyst to join our Business Intelligence team and play a critical role in shaping how data, insight, and AI are translated into commercial advantage across the Revenue Organization. This role will act as a senior individual contributor and thought partner to RevOps, Sales, Marketing, Customer Success, and Finance. The Lead Business Analyst will own the definition, delivery, and continuous improvement of commercial analytics, KPI frameworks, and executive-ready reporting.

They will work closely with Data Engineering and Systems teams to ensure data accuracy, clarity, and usability — while maintaining a strong focus on business outcomes rather than reporting/predicting output alone.

Critically, this role sits at the intersection of data, AI, and people. The ideal candidate will be as comfortable enabling a frontline sales rep to understand and act on a dashboard as they are presenting strategic insight to a C-suite audience.

They will also be a confident and curious adopter of large language model (LLM) tools — using AI to accelerate their own analytical workflow and helping the wider business do the same.

Key Responsibilities

• 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.
• Demonstrate exceptionally strong communication skills across all levels of the organisation.
• 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.
• Champion clear, consistent communication of what metrics mean, how they are calculated, and why they matter.
• Influence without authority; build credibility with senior stakeholders through the quality of insight and the integrity of the underlying data.
• 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.
• Be a power user and internal advocate of LLM-based tools, including Microsoft Copilot and Anthropic Claude.
• Help define and create LLM agents for the Go To Market organisation.
• Proactively identify opportunities to embed LLM and AI capabilities into BI workflows, dashboards, and analytical processes.
• Work with the Senior Manager of BI to evaluate and scale AI-assisted analytics approaches across the team.
• Help commercial stakeholders and frontline teams understand and appropriately trust AI-generated outputs.
• 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.
• Contribute to data governance standards, metric documentation, and reporting best practices.

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.
• Demonstrated experience designing and delivering stakeholder enablement on data, dashboards, or analytics tooling.
• 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.
• KPIs and metrics are clearly defined, consistently applied, and widely adopted across the organisation.
• LLM and AI tools are meaningfully embedded into BI workflows.
• 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.