Hire Data Analyst for Consulting Firms
Senior, direct, no layers

Consulting firms sell expertise, but much of their week goes to work that is not expertise: chasing leads, rewriting proposals, hunting for the last version of a deliverable, and rebuilding the same analysis for each new client. Partners end up doing administration, and junior staff end up recreating knowledge the firm already owns.

Clients, meanwhile, judge a firm on responsiveness and clarity. A slow proposal, a confusing deliverable or a portal nobody can log into undermines the advice itself. Firms that look disorganised in their own operations struggle to sell organisation to clients.

For consulting firms, advisory practices and boutique firms, a data analyst is useful in a specific way: dashboards for pipeline, utilisation, project margin and proposal win rates. That is the work this engagement is built around — not a generic brief with an industry name swapped in, but the job as it actually runs in this sector, where a typical engagement runs on inbound enquiries, discovery calls, proposals and contracts, kickoff and data gathering, delivery and review, invoicing, and follow-on work.

Book a free discovery call and we will test the fit before any scope is agreed.

Analysis for consulting firms works from the records they already hold in HubSpot, Notion, Miro, Calendly, PandaDoc and Asana, plus spreadsheets and exports where a system does not report well. The tooling is deliberately ordinary: spreadsheets for exploration, a business-intelligence tool or simple dashboard for recurring reports, and scripts where a repeatable clean-up is needed. The important choice is not the tool but the definition: what counts as an enquiry, a completed job or a repeat customer, agreed before the first chart is drawn, because two systems counting the same word differently is the most common source of false conclusions.

Two habits keep the work honest. The first is showing the raw counts beside any rate, so a dramatic percentage on a tiny base cannot mislead. The second is separating description from explanation: the analysis says what changed and by how much, and labels any suggested cause as a hypothesis to test, rather than presenting a correlation as a proven reason. consulting firms making decisions on these numbers deserve that distinction.

Done well, inbound enquiries, discovery calls, proposals and contracts, kickoff and data gathering, delivery and review, invoicing and follow-on work gets a clearer picture: managers stop arguing about whose spreadsheet is right, because the numbers have agreed definitions and a single source. The weekly conversation changes from reconstructing what happened to deciding what to do about it, and the segments in the report point at the specific part of the operation that needs attention rather than a general sense that things are up or down.

Analysis does not create data that does not exist, and it cannot rescue a decision that has already been made for other reasons. Where the records are too thin or too messy to support an answer, the honest result is a smaller claim and a plan to record better data, not a polished report built on guesses. That restraint is part of the work, not a failure of it.

Browse all engagements on the hire hub, or discuss this one directly via the contact page.