Top Data Analyst Jobs in Sao Paulo

Support warehouse operations by analyzing performance data, building dashboards and recurring reports, monitoring KPIs, performing ad-hoc analyses, validating data, automating reporting, forecasting labor needs, and presenting actionable recommendations to operations leadership to improve productivity and service.

Work with real-world datasets to clean, transform, and analyze data using Python and spreadsheets; perform exploratory analysis, develop data models, support client reporting, and communicate insights to cross-functional teams to drive e-commerce decisions.

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.

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 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.

Lead analytics engagements for enterprise clients using observability and digital experience data. Manage 4–8 accounts, deliver dashboards and reports, perform ad-hoc analysis, configure Dynatrace solutions, present findings to technical and executive stakeholders, and influence product roadmap and client performance strategies.

Analyze billing and support performance, build and maintain data pipelines, apply statistical forecasting and A/B testing, collaborate cross-functionally, document analyses, and provide recommendations to reduce financial risk, disputes, churn, and operational inefficiencies.

Build and own a forecasting model for clinical site payments outside Medidata Payments, link forecasts with Medidata, produce client-ready deliverables, ensure revenue recognition compliance, support audits, reconciliation at study closeout, and collaborate with FP&A, Clinical Payments, Project Analysts, and other stakeholders.

Support operational initiatives and technology projects across Direct Sales: coordinate projects, gather requirements, build solutions, automate workflows, develop reports/dashboards, validate data, troubleshoot issues, and document processes to improve efficiency and scalability.

Support warehouse operations by analyzing performance data, building dashboards and reports, automating recurring reporting, validating data, and providing actionable recommendations to improve productivity, labor utilization, inventory accuracy, and service performance. Partner with operations leadership for staffing, forecasting, and continuous improvement through statistical analysis and ad hoc investigations.

Provide data analysis and reporting as a virtual assistant: create and maintain reports, perform data entry and validation, generate visual insights, collaborate with stakeholders, track action items, and improve workflows to optimize reporting efficiency.

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

As a Data Analyst, you will deliver data solutions, optimize data pipelines, and provide actionable insights while collaborating closely with team members and stakeholders.

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.

Own end-to-end product analytics for the Pro team: build production-grade pipelines and dbt models, define north-star metrics, design A/B tests and causal inference frameworks, create dashboards and metric frameworks, embed AI tooling, and translate insights into product recommendations for leadership.

Develop analytics solutions, collaborate on requirements, manage data quality, and create visualizations while improving reporting and platforms.

Partner with product, commercial, and engineering teams to design KPIs, analyze large datasets using SQL and Python, build Power BI semantic models and dashboards, run experiments and root-cause analyses, leverage AI tools to accelerate analytics, mentor analysts, and translate findings into actionable business recommendations.

Lead source-to-target data mapping and validation for a large-scale migration to the Manhattan platform. Bridge business stakeholders and engineering, support testing and defect resolution, monitor data quality, produce migration reports and dashboards, and contribute to governance and QA throughout the migration lifecycle.

Lead stakeholder discovery, map current and future value streams, and define measurable business outcomes for data and AI initiatives. Elicit and document functional/non-functional requirements, data needs, model guardrails, and acceptance criteria. Drive pilots, UAT, change management, and client relationships while coaching teams and using generative AI to accelerate BA deliverables.

Build, maintain, and optimize data pipelines and ETL integrations (BigQuery, Fivetran, dbt). Design automations (n8n/Zapier/Make), ensure data quality and system reliability, document workflows, troubleshoot pipeline issues, and translate business needs into scalable systems to eliminate manual work.

Manage the end-to-end data issue lifecycle for North America: intake, triage, root cause analysis, remediation planning and execution oversight. Produce IMR reporting and dashboards, maintain audit-ready records, engage stakeholders, and improve IMR processes and tooling to strengthen data quality and controls.

The role involves designing business intelligence solutions in Power BI, utilizing SQL and Python for data analysis, and applying best practices in data visualization.

Lead analytics for the curriculum subscription/ecommerce business, driving GTM, retention, and financial insights. Build and maintain data models and pipelines with SQL and dbt, produce dashboards in Sigma and Tableau, own weekly business review files, and translate analyses into actionable recommendations for stakeholders.

Support global FP&A with budgeting, variance analysis, and advanced analytics. Build self-service reporting, improve FP&A processes, partner with stakeholders, and help implement and test new finance systems.

Lead development and implementation of data governance frameworks, policies, catalogs, and lineage. Ensure corporate data quality and controls, monitor governance maturity with KPIs, resolve complex data issues, consult business areas, drive stakeholder alignment, promote a data-driven culture, and provide technical leadership for teams.