**What You’ll Do** - Build use-case prioritization frameworks using quantitative scoring models that weigh business value, technical feasibility, and measurable customer impact. - Develop journey governance frameworks, testing roadmaps, and maturity assessments that help clients scale and optimize programs without accumulating architectural debt. - Design marketing analytics and KPI architectures that connect campaign-level metrics to business outcomes, including experimentation frameworks and performance narratives that enable data-backed investment decisions. - Leverage first-party data, behavioral signals, and CDP/CRM data to develop audience segmentation strategies, lifecycle stage definitions, and personalization frameworks. - Synthesize complex performance data from platforms such as Braze and turn them into actionable strategic recommendations. - Lead end-to-end journey mapping — from discovery workshops through current-state/future-state maps — defining personas, moments of truth, channel touchpoints, and data signals to surface experience gaps and prioritize improvements. - Partner with technology vendors (Braze, Segment, Databricks and others) to maintain platform intelligence and translate vendor roadmaps into client opportunity. - Lead sessions with Client Partners and Solution Architects to help clients understand how to transform their martech investments from SaaS to a consumption model purpose-built to advance client goals. - Build and present executive-ready business cases for martech investments — including ROI modeling, value realization roadmaps, and strategic narratives that translate complex technology into clear action. - Serve as a trusted strategic advisor to VP and C-level stakeholders—anticipating client challenges, identifying where additional Stitch capabilities can solve unmet client needs, partnering with Client Partners to strengthen long-term client relationships. - Participate in QBRs and EBRs to help Client Partners sequence journey investments, technology enablers, and capability-building milestones for clients. - Contribute to Stitch’s internal methodology: creating reusable frameworks, assessment tools, and workshop guides that raise the standard of every client engagement. - Author or contribute to thought leadership—LinkedIn, Webinars, and Braze Events—that position Stitch as the leading voice in modern martech strategy. - Manage your time to meet billable targets of 36+ hours per week. - Travel up to 20% for client meetings, workshops, and onsite engagements.

Role Description We are looking for a Analytics & Insights Specialist with strong technical expertise and strategic vision to lead the generation of actionable insights that drive commercial and digital marketing decisions. This role will be responsible for transforming large volumes of data into clear, compelling stories, predictive models, and recommendations that deliver measurable business impact. The successful candidate will work cross-functionally with Marketing, Product, Commercial, and Technology teams in a dynamic, innovation-driven regional environment. Role Mission: Turn data from our digital ecosystems into a competitive advantage by combining advanced analytics, machine learning, and a deep understanding of digital marketing — answering strategic business questions, uncovering growth opportunities, and effectively communicating findings to both technical and executive audiences. Key Responsibilities - Analytics & Insight Generation: - Design and execute advanced analyses on user digital behavior, campaign performance, and conversion funnels to identify patterns, opportunities, and risks. - Generate actionable insights that answer strategic business questions and translate into concrete recommendations for Marketing, Product, and Commercial teams. - Build data storytelling narratives that connect analytical findings to business decisions, tailoring the message to technical, managerial, and executive audiences. - Define KPIs, measurement frameworks, and attribution models that holistically evaluate the performance of the digital ecosystem. - Data Architecture & Analytics Engineering: - Build and maintain robust data pipelines in BigQuery from sources such as GA4, CRM, media platforms, and transactional databases. - Design efficient, optimized SQL queries on large-scale tables, including Google Analytics 4 export tables (events_*). - Collaborate with Data Engineering teams on data modeling, quality, governance, and availability across GCP and AWS environments. - Visualization & Communication of Results: - Design and maintain executive dashboards in Looker Studio (or other BI tools) that deliver clear, timely, and actionable information to stakeholders. - Present findings to C-level audiences, translating complex technical analyses into simple, actionable, business-oriented messages. - Document methodologies, assumptions, and analytical limitations to ensure transparency and reproducibility. - Collaboration & Technical Leadership: - Act as a technical reference within the Analytics & Insights team, supporting junior analysts on best practices in SQL, Python, and modeling. - Collaborate with regional teams across multiple markets, understanding local nuances and consolidating learnings at a regional level. - Promote a data-driven culture, advocating for the responsible use of data and experimentation as a driver of decision-making. Qualifications - Bachelor's or Master's degree in Statistics, Data Science, Engineering, Economics, Mathematics, Industrial Engineering, or related fields. - Relevant certifications are a plus: Google Analytics, Google Cloud (Data Engineer / Data Analyst), AWS (Data Analytics), or equivalents. Requirements - Experience in advanced analytics, data science, or business intelligence roles, preferably in digital, telecommunications, e-commerce, or media industries. - Demonstrated experience leading end-to-end analytics projects: from problem definition to implementation and communication of results. - Experience in regional or multinational environments is a strong plus. Required Technical Skills - Google Analytics 4 (GA4): configuration, implementation, advanced analysis, and BigQuery export (events_*, intraday tables, event parameters, attribution). - BigQuery / Advanced SQL: designing efficient queries, window functions, partitioning, cost optimization, and integration with GA4 and transactional data. - AWS: hands-on experience with data services such as S3, Athena, Glue, Redshift, or equivalents. - Python: data manipulation (pandas, numpy), visualization (matplotlib, seaborn, plotly), and modeling (scikit-learn, statsmodels). - Machine Learning: command of supervised and unsupervised algorithms (regression, classification, clustering), model validation, performance metrics, and experimentation. - Digital Marketing: deep understanding of the digital ecosystem (paid media, SEO, CRM, CRO, attribution, conversion funnels, customer journey). - Data Visualization: Looker Studio, Power BI, Tableau, or similar tools. Nice-to-Have Skills - Experience with dbt, Airflow, or other orchestration and analytics modeling tools. - Knowledge of experimentation platforms, CRO tools (Microsoft Clarity, Hotjar, Optimizely), and behavior analytics. - Familiarity with causal inference frameworks (CausalImpact, DiD, propensity scoring). - Experience with generative AI tools applied to analytics and productivity. Core Competencies - Analytical & critical thinking: ability to break down complex problems and draw rigorous conclusions from data. - Insight generation: ability to go beyond the metric and deliver the why and the so-what. - Data storytelling: ability to tell stories with data, structuring clear and persuasive narratives for different audiences. - Business orientation: understanding of the commercial impact of analyses and focus on generating measurable value. - Effective communication: ability to explain technical concepts simply to non-technical audiences. - Autonomy & proactivity: ability to identify analytical opportunities without detailed instructions. - Collaboration: effective work with multidisciplinary, multicultural teams. Languages - English: intermediate to advanced (B1/B2) — able to lead meetings, presentations, and produce technical documentation in English. Proyecto de 3 meses Pago por honorarios

• Own the end-to-end process of extracting and compiling member lists from multiple internal data sources — eligibility systems, pharmacy data, client records, and more — for use across email, SMS, and print communications. Your primary workspace is Snowflake, querying across the data models maintained by our Data Engineering and Analytics teams. • Define and enforce data quality standards across all member lists before they enter the delivery pipeline. Build and maintain automated checks that surface anomalies — duplicate records, missing fields, invalid formats, eligibility mismatches — before a single message is triggered. • Handle Protected Health Information with the rigor and discipline it demands. Own suppression list logic, ensuring members who have opted out, are ineligible, or fall under regulatory exclusions are accurately reflected across all lists at all times. Treat opt-out, preference, and PHI data as the highest-priority data in the pipeline. • Work hand-in-hand with the Marketing Operations Manager to ensure lists are formatted, mapped, and structured exactly to the schema required by communication platforms. Maintain an active working relationship with the centralized Data team — contributing to shared data models and quality standards, staying aligned on upstream changes, and advocating for member communications data needs at the infrastructure level.

• Atuar de forma autônoma e protagonista na resolução de problemas complexos de negócio; • Desenvolver e colocar em produção modelos preditivos e de machine learning tradicionais, de ponta a ponta; • Ser referência técnica em modelagem estatística, inferência causal e desenho de experimentos; • Entregar modelos e análises como alavancas efetivas de valor, avaliando o impacto causal de intervenções e decisões; • Conectar dados, modelos e decisões de negócios de forma pragmática; • Influenciar áreas parceiras com análises claras, estruturadas e orientadas a impacto. • Desenvolver modelos preditivos de ponta a ponta, desde o entendimento do problema de negócio até o deploy e acompanhamento produtivo; • Desenhar, implementar e acompanhar experimentos controlados, incluindo testes A/B e estratégias quase experimentais, garantindo leitura crítica e consistente dos resultados; • Desenvolver e aplicar modelos de inferência causal para estimar o impacto real de intervenções, decisões e políticas de negócio, incluindo definição de hipóteses, escolha da estratégia causal e interpretação dos efeitos estimados; • Apoiar decisões estratégicas por meio de análises orientadas a valor, conectando evidência causal, risco e retorno esperado; • Mensurar impacto das soluções entregues e traduzir resultados analíticos em recomendações claras e acionáveis para o negócio; • Garantir a sustentação, evolução e qualidade técnica dos modelos e sistemas em produção, com foco em governança, reprodutibilidade e monitoramento; • Atuar como referência técnica em ciência de dados aplicada, disseminando boas práticas e apoiando a evolução técnica do time.