Top Data Analyst Jobs in Fortaleza

Lead product data analysis for the Pro side of a home-services marketplace. Responsibilities include advanced SQL analysis, A/B testing, multivariate modeling, dashboard and reporting development, metric governance, stakeholder recommendations, and dbt-based data modeling. The role emphasizes building trusted metrics, enabling self-service analytics, influencing product decisions, and fostering a data-driven culture. It is a remote individual-contributor position requiring frequent use of AI tools and collaboration with product managers and business stakeholders.

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.

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.

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.

Own data product reliability, release quality, and customer-facing correctness across large-scale, frequently updated datasets. Responsibilities include monitoring pipeline quality, designing aggregate test plans, investigating root causes, managing cross-functional releases, communicating timelines to clients, expanding data coverage, and partnering with Commercial, Data Science, and Engineering. The role requires expert SQL, sound judgment with ambiguous data, and accountability for business outcomes.

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.

Transforms organizational data into actionable insights supporting revenue growth, operational efficiency, forecasting, and strategic planning. Responsibilities include building predictive models, developing scalable data tools, creating KPI dashboards and reports, leading end-to-end analytics projects, and presenting recommendations to senior leaders. The role partners with Finance and business stakeholders on planning and performance reviews while communicating complex findings through executive summaries and visual storytelling.

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.

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.

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.

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.

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.

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.