Top Data Analyst Jobs in Fortaleza

Perform SQL and Python-based analyses, build dashboards and reporting (Redash/Tableau/Looker/Power BI), partner with cross-functional teams to define metrics, identify opportunities, monitor performance, and use LLMs and automation to scale analytics.

Partner with cross-functional teams to define metrics, write SQL, build dashboards, perform analyses, and deliver actionable insights. Develop Python-based analytics workflows, monitor reports, and use modern AI tools to scale decision-making.

Analyze user behavior, product performance, experiments, and academic research data to guide product decisions. 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 reports, and supporting grant-funded projects.

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.

Support Data & AI Governance initiatives through data analysis, curation, stewardship, documentation, cataloging, lineage, quality improvement, and policy maintenance. Use Python for analysis and automation while collaborating with technical and business stakeholders to improve data organization, accessibility, usability, and compliance with governance standards.

Analyze observability, digital experience, and business analytics data for enterprise clients. Develop reports, dashboards, and recommendations to improve site performance and user engagement. Manage client relationships, present findings to technical and executive audiences, configure Dynatrace solutions, conduct ad hoc analysis, provide training, and represent client needs to product teams. The role combines data analysis, consulting, account management, strategic communication, and digital transformation support.

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 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.

Senior individual contributor responsible for product and marketplace analytics, designing and analyzing A/B and multivariate experiments, building dashboards and automated reports, validating instrumentation, and translating findings into executive-facing recommendations to drive growth and marketplace liquidity.

The Senior Associate Decision Scientist role involves providing analytics support by building reports and dashboards, writing SQL, and maintaining data models while collaborating with stakeholders to derive insights.

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.

The Lead Data Analyst will document technical systems, collaborate with engineering teams, manage SME availability, and produce clear, structured documentation.

Owner of Growth experimentation and acquisition-to-conversion analytics: design rigorous tests (power, sample size, anytime-valid monitoring), produce readouts, and build a Python/dbt/Lightdash self-serve experimentation layer. Maintain a trusted funnel model (visitor→lead→customer) across channels, reduce reruns via instrumentation checks, and partner with CRO, Growth PMs, and performance marketing to turn analysis into measurable conversion wins.

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.

Develop scalable, secure data pipelines and end-to-end data solutions for consulting clients. Collect, clean, transform, and analyze complex datasets; apply statistical methods; create models, visualizations, reports, and dashboards; and provide actionable recommendations. The role also involves troubleshooting system issues, supporting releases and downtimes, maintaining data quality and controls, collaborating with stakeholders, and mentoring junior developers. Remote work includes occasional infrequent travel to the United States.

Own end-to-end analyses, build trusted Snowflake and dbt reporting infrastructure, create Hex dashboards, investigate metric anomalies, ensure data quality, document metrics, partner with stakeholders, use AI responsibly, and mentor analysts.

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.

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.

Collects, models, processes, and analyzes data from multiple sources to support strategic and operational decisions. Develops dashboards, reports, KPIs, and business intelligence solutions; automates recurring processes; ensures data quality, governance, and validation; and translates business requirements into actionable insights. Collaborates with Engineering, Data, Product, and Business teams while documenting solutions and supporting data users.

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.