Top Data Analyst Jobs in Sao Paulo

Build and maintain customer-facing reports, data models, and product analyses that generate actionable insights. Partner with Product, Development, Design, UX Research, and Data Engineering teams to support data products, analytics-ready schemas, and ad hoc data needs. Apply statistical analysis, data warehousing concepts, and structured problem-solving while communicating findings to technical and non-technical stakeholders. Identify opportunities to use AI tools to automate tasks and improve data workflows.

Lead a multidisciplinary analytics team across chat, conversion, retention, AI, and operations. Develop KPI frameworks, dashboards, funnel and lifecycle analyses, experimentation strategies, and actionable product recommendations. Manage and mentor data professionals, establish the analytics roadmap, investigate ambiguous business problems, and partner with Product, Operations, AI, Growth, and leadership to improve conversion, retention, customer experience, and operational efficiency.

Leads a multidisciplinary analytics team across chat, retention, AI, and operations. Owns KPI frameworks, funnels, retention analysis, dashboards, experimentation, and operational analytics. Partners with Product, Operations, AI, Growth, and leadership to identify business drivers, improve conversion and retention, increase efficiency, and turn ambiguous questions into actionable recommendations. The role combines hands-on analysis with team leadership and stakeholder management.

Designs and optimizes Power BI semantic models, DAX standards, transformation layers, dashboards, and data pipelines. Ensures analytics reliability through data normalization, reconciliation, automated testing, and validation against source systems. Acts as a technical expert for the analytics community, reviews models, evaluates emerging and AI-assisted technologies, establishes best practices, and mentors analysts.

Lead product data analyst responsible for marketplace modeling, advanced SQL analysis, A/B testing, reporting, dashboard trust, and self-service analytics. The role partners with product managers and stakeholders to guide decisions, builds documented dbt datasets, maintains metrics and reports in Lightdash, and supports a data-driven culture. This is an autonomous individual-contributor position focused on the Pro supply side of the marketplace.

Own the growth experimentation program, including test design, power calculations, statistical rigor, monitoring, and readouts across web, SMS, sales, and SEO. Build a trusted acquisition-to-conversion funnel covering CAC, LTV, and conversion rates across channels. Extend automated experimentation tooling using Python, Lightdash, dbt, and AI workflows while partnering with CRO, marketing, and Growth PMs to turn analysis into measurable conversion improvements.

Own regulatory analytics and reporting by building audit-ready datasets, pipelines, dashboards, compliance metrics, and automated workflows. Analyze complex data, monitor quality, support audits and regulatory exams, and deliver actionable insights to compliance teams and leadership. Translate regulatory requirements into accurate reports using Python, SQL, dbt, and Airflow while partnering cross-functionally to manage data issues and remediation.

Analyze user behavior, product performance, experiments, and academic research data to guide product decisions and health outcomes. Responsibilities include building Mixpanel dashboards, validating event tracking, querying Snowflake with SQL, developing Python analyses and automation, maintaining data pipelines, creating BI visualizations, conducting user research, performing statistical testing, preparing research manuscripts, and supporting grant-funded projects.

Early-career Data Engineering Analyst supporting AIG’s Data Office through a rotational program. Responsibilities include developing cloud-based data pipelines, collecting and transforming data, building modern software systems, and supporting analytics and data science use cases. The role emphasizes data quality, security, compliance, collaboration, analytical problem-solving, and professional development through mentoring, masterclasses, and on-the-job experiences.

Lead executive discovery, process and value-stream analysis, and requirements definition for data, AI, and GenAI transformations. Facilitate workshops, identify automation opportunities, define data and model requirements, support pilots and UAT, manage stakeholder alignment, and measure business outcomes. Serve as a trusted client advisor, use generative AI to accelerate business analysis, ensure deliverable quality, and coach project team members.

Monitor transactions and customer accounts for suspicious activity, investigate suspected fraud, analyze large datasets with SQL and Python, identify fraud patterns, and develop prevention rules and controls. The role also requires documenting findings, tracking emerging threats, and using Claude or equivalent large language model tools to improve productivity, research, and communication.

Monitor transactions and customer accounts for suspicious activity, investigate suspected fraud, analyze large datasets with SQL and Python, identify fraud patterns, document findings, and develop prevention rules, strategies, and controls. The role also requires staying current on fraud threats and detection technologies while communicating complex issues clearly.

Lead a multidisciplinary analytics team across product, retention, AI, chat, and operations. Own analytics roadmaps, KPI frameworks, dashboards, funnel and cohort analysis, retention strategy, conversational metrics, and operational performance measurement. Translate ambiguous business questions into actionable insights, product recommendations, experiments, and measurable outcomes. Partner with Product, Operations, Growth, AI, Customer Experience, and leadership while developing analytical talent and improving data-driven decision-making.

Perform data quality checks, financial reconciliations, and parallel-run validation for migrated reports and dashboards. Investigate variances with Data Engineering, build Power BI quality dashboards, define acceptance criteria with Finance stakeholders, prepare delivery-gate evidence, and document rules and known issues for client handoff.

Own newsroom analytics supporting editorial, audience, and product decisions. Develop dashboards, automated reporting, measurement frameworks, deep-dive analyses, and A/B tests. Maintain reporting data quality and pipelines in partnership with engineering and product teams. Translate findings for nontechnical stakeholders, strengthen analytical practices, mentor junior analysts, and drive adoption of insights across the organization.

Write SQL queries, build dashboards and recurring reports, support newsroom analyses and A/B testing, troubleshoot data quality issues, and communicate findings to editorial, audience, and product partners. The role also involves maintaining documentation, collaborating with data engineering, and using AI tools to automate analytical workflows.

Build, analyze, and optimize hospitality reports; troubleshoot data discrepancies; interpret metrics such as ADR, RevPAR, occupancy, and channel performance; and coach customers through reporting questions. Partner with Product, Onboarding, Support, and Customer Success teams to improve data access and usability. Use advanced spreadsheets, data mapping, parsing, and root-cause analysis to support accurate financial, operational, and guest reporting.

Lead a multidisciplinary analytics team supporting product, retention, AI, chat, and operations. Own KPI frameworks, conversion funnels, lifecycle and retention analysis, conversational analytics, dashboards, experimentation, and operational performance measurement. Translate ambiguous business problems into actionable insights, product recommendations, and measurable experiments while partnering with Product, Operations, Growth, Fintech, AI, and leadership stakeholders. Establish the analytical roadmap, develop team talent, and improve data-driven decision-making across the organization.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.

The Revenue Operations Data Analyst will analyze datasets, optimize dashboards, and develop data pipelines to support revenue operations and strategic decision-making.

Develop predictive and forecasting models, scalable data tools, dashboards, and executive reports to support revenue growth, operational efficiency, and strategic planning. The role partners with Finance, senior leaders, and business teams to translate complex data into actionable recommendations, define measurable outcomes, and lead analytics projects from requirements through presentation.

Design and maintain dashboards and reports, perform deep-dive analyses to identify trends and anomalies, ensure data integrity with engineers and data scientists, automate reporting, and translate complex datasets into clear insights for non-technical stakeholders.

Partner with stakeholders to analyze large datasets, build and maintain Power BI dashboards and semantic models, perform exploratory analyses and root cause investigations, define KPIs, apply visualization best practices, and translate findings into clear narratives for technical and business audiences while leveraging SQL, DAX, and AI tools.

Build and maintain enterprise BI dashboards, reporting suites, optimized SQL queries, and analytical models. Analyze cloud data using platforms such as Snowflake, Databricks, and BigQuery, while partnering with stakeholders to define KPIs and deliver actionable insights. Manage analytical work independently within Agile sprints, ensuring data accuracy and effective collaboration across distributed teams.

Partner with Marketing, Sales, Finance, Product and other teams to turn business questions into actionable analysis. Own end-to-end analytics: build datasets and dashboards (dbt, LookML), maintain metric definitions and caveats, use and curate agent tooling for trustworthy answers, and contribute small data platform and pipeline improvements.