The KPI Architect Role in Exelab’s Tailor-Made Solutions
At Exelab, we don’t sell a single off-the-shelf product – we build custom, innovative solutions that directly target our clients’ unique business objectives. Each client engagement is essentially treated as a mini product development cycle: we start from a specific business goal or problem and craft a tailor-made solution (which might involve software, AI models, systems integration, etc.) to deliver measurable business impact.
In Exelab’s methodological framework, performance and measurability of results become central elements of every project. The KPI Architect is the cross-functional figure who ensures rigor, consistency, and depth in performance analysis across all Value projects – the high-impact strategic initiatives at the core of Exelab’s offering.
This role is not a traditional data analyst: it's a professional who combines advanced analytical skills with deep business understanding. Someone who knows that numbers tell stories, but only when you ask the right questions. The mission: ensure every project starts with clear objectives, defined measurement criteria, and a monitoring system that reveals whether actions are generating expected impact.
The KPI Architect operates across multiple projects, intervening during discovery phases to define measurement frameworks, supporting Value Builders on complex analytical challenges, and verifying the impact of deployed solutions. Reporting directly to the General Manager, maintain an overview of performance across the entire project portfolio.
In day-to-day operations, Value Builders must be autonomous in analyzing their project data. The KPI Architect intervenes when defining methodological frameworks, tackling complex analytical challenges, or ensuring client alignment on success criteria.
Key Responsibilities
The KPI Architect will ensure measurability and objectivity across our AI-enabled solutions. Core responsibilities include:
Measurement Framework Definition: Collaborate with Value Builders and Project Managers during discovery phases to define how each project’s success will be measured. Translate vague business objectives into concrete, measurable, and actionable KPIs. Establish baselines, targets, and evaluation criteria that enable objective determination of whether an initiative has generated value. Create reusable templates and methodologies that accelerate this phase on future projects.
Client Alignment on Objectives: Facilitate structured conversations with clients to ensure clarity and alignment on success criteria. Clients often have implicit or poorly defined expectations: the KPI Architect makes them explicit, quantifies them, and transforms them into shared agreements. This alignment prevents misunderstandings and creates the foundation for partnerships based on objective results.
Complex Analytical Challenge Support: Intervene when Value Builders face analytical problems requiring specialized skills or advanced methodologies. This may involve multi-touch attribution analysis, predictive modeling, advanced segmentation, or interpretation of particularly complex datasets. Bring methodological expertise and work directly on the data alongside the Value Builder.
Solution Impact Verification: After deployment, dive into the data to verify whether solutions are generating expected impact. Build the analyses personally, distinguish correlations from causality, identify confounding variables, and build rigorous analyses that enable correct attribution of results. When numbers don’t add up, propose course corrections based on concrete evidence.
Purpose-Driven Analysis: Every analysis has a clear purpose and consequent action. Don’t produce reports for the sake of producing them: every insight must translate into a decision or action. Know when data is sufficient to decide and when deeper investigation is needed. Avoid analysis paralysis while maintaining focus on business value.
Knowledge Building & Methodological Standards: Build and maintain Exelab’s analytical standards. Document methodologies, create templates, develop best practices that enable the organization to continuously improve analysis quality. Train Value Builders on fundamental analytical competencies, raising the team’s overall level.
Portfolio View & Pattern Recognition: Thanks to the cross-functional position, identify patterns emerging across multiple projects. Recognize recurring problems, reusable solutions, reference benchmarks. This portfolio view feeds Exelab’s knowledge base and accelerates future projects.
In summary, the KPI Architect is the guardian of measurability and objectivity at Exelab. This role ensures every project starts with clear objectives, proceeds with rigorous monitoring, and concludes with impact verification. The work transforms the promise of "measurable value" from a marketing claim into demonstrable reality.
Requirements
Ideal Candidate Profile – Skills and Experience
Our ideal candidate is a professional with a rare combination: technical excellence in data analysis combined with deep understanding of business dynamics. It’s not enough to know how to manipulate data – you need to know which questions to ask and how to translate answers into valuable actions. Below are the key characteristics and qualifications we expect:
Advanced Analytical Skills: Mastery of data analysis tools and methodologies: SQL, Python/R for statistical analysis, BI tools (Looker, Tableau, Power BI). Comfort with statistical analysis: significance testing, regression analysis, cohort analysis, A/B testing methodology. Ability to work with large datasets and heterogeneous sources. Experience building dashboards and monitoring systems.
Business Acumen & Strategic Orientation: Deep understanding of how businesses work and which metrics truly matter. Experience with KPIs typical of customer-facing contexts: conversion rate, customer acquisition cost, lifetime value, churn, NPS. Ability to link operational metrics to financial impact. Skill in translating vague business objectives into concrete, measurable indicators.
Critical Thinking & Methodological Rigor: Scientific approach to analysis: formulate hypotheses, test them, evaluate evidence with constructive skepticism. Distinguish correlation from causality and identify biases and confounding variables. Don’t settle for the first interpretation: dig until finding the correct explanation. At the same time, know when data is “good enough” to decide.
Communication & Stakeholder Management: Exceptional ability to communicate complex insights clearly and actionably. Adapt the message to the audience: executive summary for C-level, methodological detail for technical teams. Skill in facilitating difficult conversations about success criteria, managing expectations and building alignment.
Consulting or Multi-Project Experience: Background in environments involving multiple projects/clients in parallel. May come from consulting firms, agencies, or analytics roles in strongly data-oriented companies. Experience across diverse contexts develops pattern recognition and adaptability. Familiarity with CRM and marketing technology is a significant plus.
Autonomy & Proactivity: Ability to operate with minimal supervision, proactively identifying where their intervention can create value. Don’t wait for problems to arrive: anticipate analytical needs and step forward. Manage own time across multiple projects, prioritizing based on impact.
AI Openness & Continuous Learning: Willingness to learn and use AI tools to enhance analytical activities. Intellectual curiosity and drive for continuous improvement. Data analysis evolves rapidly: we’re looking for someone who embraces this change as opportunity. Native Italian and at least B2-level English required.
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