KPI Architect

Exelab builds custom, AI-powered solutions, each one tailor-made to a client's specific business objective. We run every engagement like a small product effort: we start from a real problem and craft a solution to move a real metric (revenue, efficiency, conversion, retention). We work in small, tight teams, close to the client's business, and we use AI directly in how we design and build. What matters to us is the impact we create for clients. 

The role works across selected client engagements rather than being assigned indiscriminately to every project. It is most relevant when Exelab aims to create a measurable business, operational, or behavioral outcome. Projects whose success is defined primarily by technical delivery, regulatory compliance, or adherence to fixed requirements may not require the same level of involvement.

The KPI Architect may join before the sale to help Value Builders and account teams validate an opportunity, at project inception to turn a value objective into a measurement framework, or later when complex analysis, a dashboard, or a monitoring system is needed. Reporting to the General Manager, this person works alongside Value Builders, Project Managers, Solution Architects, and technical specialists. The Value Builder remains accountable for the value delivered; the KPI Architect is accountable for the integrity and credibility of how that value is defined and measured.

The KPI Architect ensures that selected client initiatives can be evaluated through reliable data and useful decision systems. Their core responsibilities include:

• Opportunity Validation & Success Definition: Support Value Builders and account teams during discovery and, when useful, before the sale. Clarify the business problem, test whether a credible value hypothesis exists, identify what evidence would demonstrate success, and turn implicit expectations into explicit and shared criteria.

• Measurement Architecture: Translate business objectives into a coherent measurement framework. Define baselines, targets, leading and lagging indicators, calculation logic, data sources, ownership, review cadence, and evaluation criteria. Connect operational measures to the business or financial outcome they are intended to influence.

• Data & Monitoring System Design: Design the data models, collection mechanisms, dashboards, and monitoring flows required to make the framework operational. Build prototypes and fit-for-purpose solutions directly; when production-grade pipelines, integrations, infrastructure, or security require deeper engineering, work with Solution Architects and data specialists to define and validate the implementation.

• Hands-On Analysis & Complex Problem Solving: Work directly with data to address analytical questions that exceed the project team's routine capabilities. Depending on the case, this may include segmentation, cohort analysis, experimentation, attribution, forecasting, regression, or the interpretation of heterogeneous data sources. Use the level of sophistication the decision requires, without creating analysis for its own sake.

• Impact Verification & Decision Support: After deployment, determine whether the solution is producing the expected effect. Distinguish signal from noise, correlation from causation, and real impact from coincidental movement. Explain findings and uncertainty clearly, and turn the evidence into decisions, course corrections, or new hypotheses.

• Standards, Enablement & Portfolio Learning: Create reusable methods, templates, and quality standards that improve measurement across Exelab. Help Value Builders and other roles become autonomous on ordinary cases, while remaining available for higher-complexity work. Use the cross-project view to identify recurring patterns, benchmarks, data gaps, and reusable solutions.

In summary, the KPI Architect is the person who turns the promise of measurable value into a working measurement system. This is not a reporting factory and not the sole owner of every project's data. Success means that relevant projects start with credible measures, Exelab can demonstrate the value created, and project teams become increasingly capable of handling routine measurement independently.

Our ideal candidate combines consulting ability, analytical rigor, and practical data skills. We are not looking for one prescribed career path: the right person may have worked in analytics consulting, decision science, value engineering, business intelligence, data analytics, or another environment where business questions had to be translated into reliable evidence and operational systems.

• Business Discovery & Consulting Ability: Able to lead structured conversations with clients, including senior stakeholders, and move from a vague objective to a precise problem worth measuring. Asks incisive questions, challenges weak assumptions constructively, and creates alignment without hiding behind technical language.

• Hands-On Analytics: Strong practical command of SQL and at least one analytical programming environment such as Python or R, together with experience using BI and visualization tools such as Power BI, Tableau, or Looker. Comfortable preparing and exploring data, applying sound statistical methods, and building analyses and dashboards personally.

• Measurement & Data-System Thinking: Understands the complete chain from business objective to KPI, data capture, model, dashboard, interpretation, and decision. Can design robust measurement systems and implement fit-for-purpose solutions. Experience with production data models, pipelines, data quality, CRM or marketing technology, and cloud data platforms is valuable; deep data-engineering ownership is preferred, not mandatory.

• Critical Thinking & Methodological Judgment: Formulates hypotheses, tests them, recognizes bias and confounding variables, and distinguishes correlation from causation. Knows when evidence is strong enough to act and when a deeper investigation is justified. Applies rigor pragmatically rather than pursuing complexity for its own sake.

• Communication & Stakeholder Influence: Communicates complex findings clearly to both executives and technical teams. Can explain uncertainty, defend a methodological choice, challenge a misleading metric, and turn analysis into a concrete recommendation. Builds credibility through clarity and evidence rather than authority.

• Multi-Project Autonomy: Operates effectively as an experienced individual contributor across several projects and stages, prioritizing involvement where it creates the most value. Consulting, agency, system-integration, or other multi-client experience is particularly relevant. Formal seniority matters less than evidence of independent judgment and reliable client-facing work.

• Learning Agility & AI-Native Working: Learns unfamiliar business domains quickly and uses AI tools responsibly to accelerate analysis, coding, documentation, and prototyping. Remains accountable for the quality of the reasoning, the implementation, and the conclusions.

• Native Italian and at least B2-level English required.

In summary, an ideal candidate profile might read: "A client-facing analytics professional who can clarify what success means, design the system that will measure it, work directly with data to build the analysis or dashboard, and explain what the evidence means for the next business decision."

Information Security Responsibilities: All employees are required to comply with Exelab's Information Security Policy, report security incidents promptly to the CISO, handle data according to its classification, and complete security awareness training.