Data Consultant

About the role

Lahzo is changing how businesses grow through AI‑powered sales and marketing solutions. Founded in 2023 and backed by strong investors, we’ve built a revenue‑growth flywheel that combines enterprise‑grade AI Sales Agents with hyper‑targeted digital advertising. As a forward‑deployed data specialist, you will work inside each client’s systems—CRM, DMS, POS, EHR—to establish a trustworthy trace from a lead to a real sale, verify data quality, prove lead‑to‑sale ROI, perform ad‑hoc analysis, and turn client‑specific learnings into repeatable processes for faster onboarding.

Responsibilities

• Understand each client's data systems: map how data flows, where leads land, how records are created and re‑keyed, what a “sale” looks like, and which fields are trustworthy.
• Establish the data connection: work with the client (and possibly their DMS/CRM vendor) to get sales data flowing to Lahzo on a recurring basis, keyed so leads and sales can be matched.
• Verify the data is right: validate client‑supplied data without seeing its source system, reconcile across deliveries, anchor on stable identifiers, and detect records that change or disappear.
• Prove lead to sale: build matching logic that connects leads to closed deals, quantify unmatched leads and why, deliver an ROI answer the client believes, and define what needs to be built to support it ongoing.
• Answer ad‑hoc questions: scope the question, write SQL, deliver actionable answers on lead quality, funnel drop‑offs, segment conversion differences, etc., judging the appropriate rigor and confidence.
• Make the next client faster: turn bespoke investigations into repeatable approaches, standard data requests, known failure modes by system type, and clear definitions of a good client feed.

Requirements

• Background in technical or implementation consulting with at least 5 years of experience: professional services, solutions delivery, technical account management, or client‑facing data work at a vendor.
• Strong analytical ability: take an ambiguous question, determine what would actually answer it, and build that answer from imperfect data, segmenting, comparing across time, and sizing findings.
• Track record of ad hoc analysis on a real timeline: scope a half‑formed question, query it, and return an answer with an honest read on trustworthiness.
• Habit of verifying data you didn’t produce: reconcile, cross‑check across time, and look for what’s missing rather than confirming what’s present.
• Demonstrated ability to orient quickly in unfamiliar operational systems: understand not just tables but how people actually use the system, and therefore where the data breaks.
• Advanced, hands‑on SQL: multi‑table