Top Data Analyst Jobs in Mexico

Analyze large internal and external sales datasets to identify trends, perform root-cause analysis, build forecasts, develop KPIs, and deliver dashboards and visualizations to support sales and cross-functional decisions.

Integrate and validate marketing data from multiple sources, ensure accurate campaign tracking and attribution, analyze paid media performance, support A/B testing, build scalable dashboards and pipelines, and partner with marketing and product teams to define KPIs and recommend optimizations.

The Machine Learning Analyst collaborates with AI/ML teams to identify data requirements, implements data ingestion pipelines, and ensures data quality while supporting data scientists in model development.

The Data Analyst will manage and update information on ECUs, ensuring technical coherence and compliance with deadlines while coordinating with teams.

Lead digital projects focusing on automation and data insights, mentor teams, develop technical solutions, and ensure project alignment with business goals.

The Data Analyst role focuses on leveraging advanced analytics to address strategic business challenges, including designing and implementing analytical solutions.

Senior data analyst responsible for statistical modeling, EDA, forecasting, building complex reports and visualizations, assembling large datasets, troubleshooting data issues, performing QA, and improving data systems and forecasting techniques.

Analyze transactional and operational data to detect and reduce fraud, build and maintain fraud rules/features/models, create dashboards and reports, perform human-in-the-loop reviews, and partner with Risk Operations, Payments, Growth, and leadership to improve processes and mitigations.

Analyze fraud patterns and metrics to reduce fraud losses and false positives. Build dashboards, fraud features, rules, and models. Perform human-in-the-loop reviews, improve operational processes, and provide actionable insights to Risk Operations, Payments, Growth, and senior management.

Analyze fraud trends and monitor metrics (fraud losses, false positives). Build dashboards, features, rules, and models to detect and prevent fraud. Perform human-in-the-loop reviews, collaborate with Risk Operations, Payments, Growth, and senior management, and improve operational feedback loops.

Support D2C and B2B growth by building automated dashboards, performing funnel and conversion analyses, tracking lifecycle marketing and ad performance, and partnering with cross-functional teams to scale growth initiatives. Learn from mentors and progress into owning growth and marketing analytics domains.

Build and maintain analytics data models, dashboards, and reports. Use SQL (Postgres, Trino, Iceberg) and dbt to power financial metrics. Perform deep-dive analyses using SQL and Python (Jupyter, Pandas) to answer business questions, operationalize data, and improve reporting and automation.

Use advanced SQL and Python to analyze fleet-scale manufacturing, test, and operational data. Build KPIs, statistical analyses, dashboards, and AI-driven automations (anomaly detection, recommendations). Partner with engineering, product, and suppliers to improve data quality, embed AI into NPI and manufacturing processes, and deliver executive summaries and decision-ready operational insights.

Transform large datasets into actionable insights using advanced SQL and cloud data warehouses (Snowflake). Build dashboards and visualizations, perform spreadsheet analysis, troubleshoot data quality issues, apply AI/ML tools for analysis, and present clear findings to stakeholders.

The Commercial Data Analyst will support revenue optimization and data-driven projects, manage reporting tools, and collaborate with stakeholders to provide actionable insights based on data analysis.

Maintain and consolidate master part and logistics data, produce reports and dashboards, identify mismatches, support customs compliance, analyze supplier performance, calculate cost savings, and recommend process and system improvements to increase efficiency and reduce costs.

Analyze campaign performance, define KPIs and measurement frameworks, explore data and generate insights, build simple analytical models (segmentation, propensity, regression), and support measurement, experimentation, and optimization initiatives for media and marketing clients.

Perform statistical modeling and analysis of structured and unstructured data, create visualizations and dashboards, assemble and QA large datasets, troubleshoot database issues, generate monthly Excel/Google Sheets reports, and work with analytics teams to improve data systems and accuracy.

Develop, maintain, and enhance ~50 Project Engineering Power BI reports; connect and validate Snowflake/SAP/PWA data; create new dashboards from user requirements; perform ad-hoc Excel and Power BI analyses; act as reporting SME, review usage, provide training, and deliver data-driven insights to stakeholders.

The Product Analyst will provide insights to empower Product Managers, analyze user data and trends, drive business recommendations, and support product initiatives and engagement tracking.

The role focuses on developing reporting functions and measuring media performance, transforming data using Snowflake, dbt, and analyzing with R or Python.

The Data Analyst III will analyze both structured and unstructured datasets, create visualizations, generate reports, perform quality assurance, and troubleshoot database issues to support data infrastructure needs.

The Data Analyst, Growth will analyze marketing data to deliver actionable insights, design dashboards, automate reporting, and support cross-functional projects to improve client acquisition and retention.

Drive AI-ready data foundations by translating business needs into scalable data solutions. Improve data flows, enable automated analytics and ML use cases, support governance, partner with engineering to build pipelines and data products, and lead validation, profiling, and adoption activities.

The Salesforce Data Hygiene Analyst ensures the accuracy and consistency of Salesforce data by resolving quality issues, developing processes, and providing analytics support.