Analytics Engineer

• FBS – Farmer Business Services is part of Farmers operations with the purpose of building a global approach to identifying, recruiting, hiring, and retaining top talent.
• By combining international reach with US expertise, we build diverse and high-performing teams that are equipped to thrive in today’s competitive marketplace.
• We believe that the foundation of every successful business lies in having the right people with the right skills. That is where we come in—helping Farmers build a winning team that delivers consistent and sustainable results.
• Since we don’t have a local legal entity, we’ve partnered with Capgemini, which acts as the Employer of Record. Capgemini is responsible for managing local payroll and benefits.
• A solid and innovative company with a strong market presence
• A dynamic, diverse, and multicultural work environment
• Leaders with deep market knowledge and strategic vision
• Continuous learning and development

Associate Analytics Engineer

• Work with Python and Spark for large-scale data processing, using Airflow as the orchestration tool.
• Own extraction and integration pipelines on AWS, operating in distributed environments such as Kubernetes and EMR.
• Work on data storage and analytical layers using SQL, S3 and Trino, supporting data modeling and analytical queries.
• Participate in defining and maintaining data infrastructure with Terraform, backed by a proprietary Infrastructure-as-Code (IaC) framework.
• Collaborate with engineering, analytics and business teams on Spark job tuning and optimization, pipeline versioning and standardization, and contribute to platform observability, governance and scalability best practices.

Analytics Engineer, Mid-Level

• Apoiar a construção e manutenção (processo e modelagem completa) do nosso data lake nas camadas silver e gold.
• Apoiar a construir e manter pipelines de dados robustos e escaláveis.
• Automatizar processos de coleta, transformação e integrações de dados.
• Garantir a qualidade, consistência e governança completa dos dados.
• Trabalhar em parceria com analista, cientista de dados, engenheiro e stakeholders de produto.
• Realizar refinamento, documentação e catalogação de projetos e métricas.
• Apoiar ativamente as áreas de negócio com dados confiáveis para tomada de decisão.
• Atuar e construir ativamente a partir da camada gold de dados dashboards estratégicos.

Engenheiro de Analytics Sênior

• Senior Data Developer (Analytics Engineer) who serves as a bridge between business and technology
• Own the complete lifecycle of data products – from ingestion and modeling through final delivery
• Ensure that data is structured, validated, and ready for consumption in AI and analytics environments
• Actively engage with business stakeholders to understand context, challenges, and underlying needs
• Translate complex requirements into clear, detailed technical specifications that enable effective use of AI and analytics platforms
• Take full responsibility for the data products built – from ingestion through modeling, transformation, validation, to final delivery and ongoing monitoring
• Implement and maintain rigorous validation processes, automated testing, and quality checks to ensure data accuracy and integrity
• Design and implement data transformation layers that pre-aggregate business information
• Collaborate with AI teams to specify and prepare structured data, data dictionaries, and models that enable AI platforms and intelligent reporting capabilities
• Build and maintain data ingestion pipelines from multiple sources, implement transformations in SQL and Python
• Proactively identify improvement opportunities, anticipate internal customer needs, and propose innovative solutions
• Deliver reliable data solutions with speed and precision, balancing the need for rapid iteration with high quality standards
• Articulate technical concepts clearly to non-technical audiences, facilitate alignments, present solution proposals, and document modeling and architecture decisions

Senior Data Developer, Analytics Engineer

• Azure Data Engineering: Design and maintain data pipelines and solutions using Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and Azure Data Lake, ensuring end-to-end reliability, scalability, and performance.
• Analytics & Visualization: Build dashboards and analytical reports in Power BI and Tableau, translating infrastructure metrics, application performance data, and business KPIs into concrete, actionable recommendations.
• Data Modeling & Integration: Develop data modeling, data mining, and integration processes across on-premises and cloud sources, creating reliable datasets to support analytical models and dashboards.
• Queries & Large-Scale Processing: Write complex SQL and Kusto queries, as well as scripts and REST API calls for large-scale data collection and processing.
• Cloud Cost Management: Operate with cost awareness in managing cloud compute and storage resources, proposing optimizations that balance performance and efficiency.
• Technical Reference: Guide application and business teams on data usage and interpretation, proactively promoting best practices in data engineering, modeling, and BI tooling.
• Agile Collaboration: Work within Agile/Scrum methodologies using project management tools such as Jira, with the ability to prioritize deliverables and manage tasks autonomously across onsite and offshore teams.

Senior AZURE Data Engineer/Analyst

• Ajudar grandes empresas a transformar o potencial da AI em impacto real nos negócios com AI Deployment, execução AI-native e tech-integrated business solutions.
• Desenvolvimento e sustentação do principal canal de investimentos de um banco.
• Foco principal no desenvolvimento de aplicações utilizando a linguagem .net para o backend e Angular para frontend.

Mid Level/Senior Analytics Engineer

• Lead the migration and structuring of data from the Credit, Collections and Fraud Prevention areas to Google Cloud Platform (GCP)
• Build refined tables and automate processes using the various tools available in GCP
• Oversee system integration projects focused on process automation and data structuring
• Generate new insights from data analysis and communicate findings clearly to business stakeholders
• Monitor the performance of Collections & Fraud (C&F) ML models through automated data processes
• Create innovative processes using GenAI and Predictive AI within the Directorate

Data and Analytics Specialist, I

• Develop and maintain data pipelines (ETL/ELT) using Databricks, Azure Data Factory and other components of the Azure ecosystem
• Model and structure data in Data Lakes (ADLS), ensuring data quality, traceability and governance
• Ingest and process data from multiple sources (APIs, monthly files, corporate systems)
• Implement data solutions for:
• PETROS Database (extraction and organization of pension data)
• Structuring RAG for analysis of debt contracts
• Processing quotation/price data (integration with external APIs such as CXL)
• Collateral projects (judicial deposits, financial and real guarantees)
• Reconcile purchases and sales with analysis of structured and unstructured data
• Support architects in defining solutions and designing data architecture
• Participate in gathering and detailing technical and functional requirements
• Develop and publish APIs to expose data
• Ensure best practices for versioning, CI/CD and DevOps
• Work with relational databases (SQL Server, Oracle, among others)

Senior Data Engineer – Data & Analytics

• Data Engineering & Architecture — Own the foundational marketing data layer. You will design, build, and maintain the data pipelines that transform raw marketing sources into optimized dimensional models (utilizing Medallion architecture), delivering clean, report-ready tables for downstream business intelligence and analytics;
• Predictive Analysis — Develop and apply statistical and predictive models to forecast key marketing funnel metrics, identify areas for improvement, and inform strategic decision-making;
• Marketing Attribution — Support the design and implementation of marketing attribution models to accurately measure channel and campaign contribution;
• Data Quality & Governance — Ensure data reliability through monitoring, validation, cleansing, and adherence to data governance policies;
• Documentation & Versioning — Maintain thorough documentation of the marketing data ecosystem and version data projects in GitHub;
• Semantic Layer for AI — Maintain and extend BI data models (LookML and/or dbt preferred) enriched with metadata, business context, and structured definitions that make them consumable by AI agents and natural language analytics interfaces;
• Reusable AI Skills & Workflows — Build reusable AI skills, tools, and workflows that allow analysts and stakeholders to interact with governed data through natural language interfaces;
• AI Quality Assurance — Run structured test cycles to validate AI agent output accuracy, identify regressions, and maintain quality standards as models and data evolve;
• Data Standards for Responsible AI — Contribute to metadata standards and responsible AI guidelines that govern how marketing data is surfaced through automated and AI-assisted analytics.

Senior Marketing Analytics Engineer

• Liderar a Última Milha: Gerir o time responsável pela camada Gold, garantindo ativos de dados e Features versionadas, governadas e prontas para escala.
• Guardião das Métricas: Atuar como a autoridade final na precisão e consistência dos indicadores. Implementar um Protocolo Unificado de Governança de Métricas para garantir que a IA e o BI falem a mesma língua.
• Análises de Negócio de Alto Impacto: Conduzir o desenvolvimento de análises que revelem o comportamento do shopper, gerando insights acionáveis tanto para o varejo alimentar quanto para parceiros da indústria.
• Orquestrar o Encantamento: Liderar o desenvolvimento de dashboards de alta performance (Power BI/QuickSight) e soluções de IA Agêntica que permitam o autoatendimento de dados em tempo real.
• Liderança com Propósito: Inspirar e guiar a evolução técnica e profissional do time, cultivando talentos e garantindo um ambiente de alta performance e colaboração.
• Cultura de Melhoria Contínua: Aplicar ciclos PDSA (Plan, Do, Study, Act) para reduzir a variância operacional e elevar a qualidade das entregas aos nossos clientes.
• IA com Empatia: Liderar a aplicação de IA Generativa para criar insights ultra-relevantes, agindo como um "motor emocional silencioso" que amplifica a sensibilidade organizacional.

Gerente de Data Analytics, Analytics Engineering

• Atuar diretamente em tribos que necessitam de construção de pipelines e novos dash's.
• **Experiência Sólida como Engenheiro(a) Analítico(a)**, Engenheiro(a) de BI ou função similar.
• Tempo de Experiência Mínimo Comprovado: Entre 2 e 5 (obrigatório).
• **Domínio de SQL:** Conhecimento avançado em estruturas, otimização de *queries* e modelagem dimensional/relacional.
• **Experiência Avançada com DBT:** Uso profissional na construção, testes e documentação de *pipelines* de transformação de dados.
• **Experiência com Airflow:** Construção, manutenção e orquestração de *pipelines* complexos de dados.
• **Google Cloud Platform (GCP):**
• **Expertise em BigQuery:** Conhecimento aprofundado em performance, *cost management*, e recursos avançados da plataforma.
• Familiaridade com outros serviços de dados do GCP (ex: Google Cloud Storage).
• **Experiência com Plataformas de BI/Visualização:** Proficiência no desenvolvimento de *dashboards* no **Looker Platform**.

Analytics Engineer – Pleno

• Work directly within tribes requiring the development of data pipelines and new dashboards.
• Strong experience as an Analytics Engineer, BI Engineer, or in a similar role.
• Minimum proven experience: 5 to 6 years (required).
• Advanced SQL: deep knowledge of schema design, query optimization, and dimensional/relational modeling.
• Advanced experience with DBT: professional use in building, testing, and documenting data transformation pipelines.
• Experience with Airflow: building, maintaining, and orchestrating complex data pipelines.
• Google Cloud Platform (GCP):
• Expertise in BigQuery: in-depth knowledge of performance tuning, cost management, and advanced platform features.
• Familiarity with other GCP data services (e.g., Google Cloud Storage).
• Experience with BI/visualization platforms: proficiency in developing dashboards on the Looker Platform.

Senior Analytics Engineer

• Prepare technical architecture and design documents;
• Develop data modeling;
• Manage the Databricks environment;
• Manage AWS, Azure and GCP cloud components;
• Develop solutions using (Java / Python / Spark / Scala);
• Configure network components in hybrid environments;
• Define best practices for relational and non-relational data storage;
• Implement improvements in Data Lake and Data Warehouse environments;
• Implement and manage SQL and NoSQL databases;
• Define and develop DevOps, Docker and Kubernetes solutions;
• Discuss and define the best solutions for system integrations;
• Support the creation of data governance processes;
• Advise on the best solutions to support the company's self-service BI strategy.

• Technical and People Leadership: Coordinate and support the development of the Analytics Engineering team.
• Data Architecture and Modeling: Ensure that solutions are built according to established data modeling principles.
• Cloud Data Warehouse Governance: Oversee the organization, cost management, and performance of the deployed data environments.
• Product Management and Delivery: Act with a strong sense of ownership over data products.
• Hands-on: Support the technical team on complex architectures and data validations through strong SQL expertise.

Analytics Engineering Coordinator – BigQuery, Snowflake

• Apoiar o desenho, desenvolvimento e manutenção de soluções analíticas.
• Participar da preparação dos dados e da construção de modelos e relatórios em Power BI.
• Desenhar, desenvolver e manter processos de extração, transformação e carga de dados para soluções analíticas.
• Trabalhar com diferentes fontes de informação, garantindo qualidade, consistência e disponibilidade dos dados.
• Desenvolver modelos de dados orientados a reporting, análise e tomada de decisão.
• Construir modelos semânticos em Power BI, aplicando boas práticas de modelagem, DAX, performance e reutilização.
• Desenvolver dashboards, relatórios e indicadores para diferentes níveis da organização.
• Levantar requisitos com usuários de negócio, entendendo suas necessidades e traduzindo-as em soluções analíticas.
• Validar informações, regras de negócio, KPIs e resultados junto aos usuários-chave.
• Documentar processos, regras de transformação, definições de indicadores e modelos desenvolvidos.
• Colaborar com o time de Analytics na evolução da arquitetura atual baseada em Microsoft Fabric e Power BI.

Analytics Engineer – Power BI, Microsoft Technologies

• Build and maintain high-quality LookML models, ensuring the semantic layer is enriched with accurate labels, descriptions, synonyms, and business logic that AI tools — including Looker Conversational Analytics and external LLMs — can reliably consume;
• Design and configure domain-focused Data Agents with well-structured instructions, Golden Queries, and scoped context governance to deliver accurate and trustworthy conversational analytics;
• Develop and maintain CI/CD pipelines that automate metadata governance, including AI-assisted description generation, DDL synchronization, and semantic consistency checks;
• Build reusable AI skills, tools, and workflows that allow analysts and stakeholders to interact with governed data through natural language interfaces;
• Partner with Data Analysts, Data Engineers, and business stakeholders to translate vague or poorly defined business questions into well-defined, measurable data models and agent instructions;
• Identify opportunities to improve the quality and reliability of AI-generated insights by improving context structure, metadata coverage, and semantic layer precision;
• Develop recommendations and drive improvements to data standards, processes, and definitions;
• Contribute to cross-team guidelines and best practices for responsible AI usage with data.

Senior AI Analytics Engineer

• Design, build, and maintain scalable data models in Amazon Redshift to support reporting, dashboards, and ad hoc analysis.
• Collaborate with data engineering teams to expand and optimize the analytics warehouse architecture.
• Establish and enforce standards for schema design, naming conventions, and data lineage.
• Develop, maintain, and optimize data pipelines that deliver clean and reliable data to business users.
• Partner with stakeholders to gather requirements and translate business needs into scalable data solutions.
• Create and maintain comprehensive data documentation, including metrics definitions and transformation logic.
• Support BI platforms by ensuring analytics-ready datasets and assisting with dashboard development.
• Monitor data quality and implement testing, alerting, and validation processes to ensure warehouse reliability.
• Identify opportunities to improve data accessibility, consistency, and usability across the organization.
• Promote best practices in analytics engineering, including version control, code reviews, and reproducible workflows.

Analytics Engineer – Digital Media

• Structure and evolve the corporate analytics layer, transforming data already available on the platform into reliable, organized, metrics-oriented assets.
• Analytics Modeling & Metrics: Develop and evolve dimensional and metric-driven models.
• Define and standardize corporate KPIs.
• Build data marts oriented to business domains.
• Define and maintain a semantic layer for consumption by BI tools and analytical applications.
• Document business rules and metric calculations.
• Solution and Data Product Development: Design and evolve data products based on certified metrics.
• Create analytics-ready datasets for consumption.
• Establish reliable data foundations to support AI initiatives and predictive models.
• Collaborate with business areas in designing data-driven solutions.
• Quality, Governance & Performance: Implement quality tests in the analytics layer.
• Ensure traceability and consistency of metrics.
• Ensure adequate performance for analytical consumption.
• Strategic Engagement: Work closely with Product, Operations and Finance to define strategic metrics.

• Participate in vendor scoping sessions to finalize full table and field requirements before the build phase begins.
• Model a dedicated datamart in the dbt transformations marts layer, sourcing data from core order, line item, and customer tables.
• Translate vendor schema specifications into specific internal field mappings, accurately accounting for DTC-adjusted logic such as ship-to addresses, order dates, and customer grain.
• Configure Snowflake data sharing and connections to facilitate seamless vendor ingestion.
• Write comprehensive dbt tests and documentation for all delivered models to ensure long-term data quality and transparency.
• Manage the full merge request workflow using GitLab, adhering to strict peer review processes before deployment.

Senior Analytics Engineer – ETL/ELT Focused

• Liderar o desenho e a concepção de arquiteturas de dados de missão crítica.
• Translate requisitos complexos de negócios em soluções tecnológicas de ponta.
• Liderar a definição técnica dos projetos.
• Calcular esforços e custos de infraestrutura em nuvem.
• Garantir que a adoção de novas tecnologias de dados entregue máxima eficiência, escalabilidade e satisfação aos demandantes internos.

Analytics Engineer jobs in Brazil

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Associate Analytics Engineer

Analytics Engineer, Mid-Level

Engenheiro de Analytics Sênior

Senior Data Developer, Analytics Engineer

Senior AZURE Data Engineer/Analyst

Mid Level/Senior Analytics Engineer

Data and Analytics Specialist, I

Senior Data Engineer – Data & Analytics

Senior Marketing Analytics Engineer

Gerente de Data Analytics, Analytics Engineering

Analytics Engineer – Pleno

Senior Analytics Engineer

Analytics Engineer

Analytics Engineering Coordinator – BigQuery, Snowflake

Analytics Engineer – Power BI, Microsoft Technologies

Senior AI Analytics Engineer

Analytics Engineer – Digital Media

Senior Analytics Engineer

Senior Analytics Engineer – ETL/ELT Focused

Engenheiro de Soluções de Dados