Analytics Engineer

• 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.

Senior Analytics Engineer

• 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.

Engenheiro de Soluções de Dados

• Lead the design and conception of mission-critical data architectures.
• Translate complex business requirements into cutting-edge technological solutions.
• Lead the technical definition of projects.
• Estimate cloud infrastructure effort and costs.
• Ensure adoption of new data technologies delivers maximum efficiency, scalability and satisfaction to internal stakeholders.

Data Solutions Engineer

• Development and execution of data transformation models (ETL/ELT) to produce strategic business metrics.
• Join the Clicksign Data team, acting as the bridge between Data Engineers and Data Analysts.
• Implement automated tests to continuously maintain data reliability.
• Create clear and concise documentation to ensure data consumers' autonomy and support team scalability.
• Serve as a reference for validating models and analyses produced by peers.

• Develop, maintain and evolve data pipelines for collecting, ingesting, transforming, and delivering information in an analytical environment;
• Ensure data quality, integrity, and consistency through validations, monitoring, and handling pipeline failures;
• Process and analyze large volumes of data using Databricks, SQL, and advanced transformation and analytical exploration techniques;
• Identify patterns, trends, and optimization opportunities from data to support the improvement of processes and business outcomes;
• Develop and optimize code to automate analytical processes and data integration;
• Work on optimizing operational processes, reducing manual tasks, and increasing the efficiency and scalability of routines;
• Structure, document, and evolve data flows and processes, identifying bottlenecks, dependencies, and opportunities for continuous improvement;
• Create interactive Power BI dashboards that meet the needs of business areas;
• Collaborate with marketing, sales, and product teams to align metrics and objectives.

• This is not a role focused solely on processing data or building complex dashboards. We are looking for a professional who will act as the official “translator” of data for the business. You will be the right hand of strategy, turning SQL into persuasive narratives and recommendations that directly move the company needle.
• Your mission will be to “hunt” for opportunities through consumer behavior and market studies, ensuring that data answers business questions in an intelligent, visual, and strategic way.
• Impact Analysis: Conduct deep studies on consumer profiles and market behavior.
• Executive Storytelling: Turn complex data into clear, fluid, and persuasive presentations (emphasis on PowerPoint and oral presentations).
• Influence and Advocacy: Defend recommendations and actively participate in discussion forums and meetings with executive leadership.
• Business Partnership: Work with a product/business mindset closely alongside UX Designers, ensuring analysis focuses on the customer experience.
• Versatile Interface: Connect with multiple areas to understand pain points and propose evidence-based solutions, supporting financial and performance decisions.
• Design and build scalable, resilient data analytics solutions in the GCP environment;
• Define strategies for implementing and building key metrics;
• Propose and implement architectural improvements;
• Gather requirements from business areas and document them;
• Be familiar with agile methodologies;
• Align the corporate IT environment with business needs, standards, and market best practices;
• Be capable of embedding a data-driven culture into day-to-day activities;
• Have the ability to work in a team with empathy, be able to listen and be heard, and possess strong communication skills.
• Be adaptable to dynamic work scenarios;

Senior Analytics Engineer, GCP

• Build and maintain robust data models using dbt
• Develop fact tables, dimension tables and data marts aligned with business needs
• Serve as the bridge between business teams and data engineering
• Document developed models, including metric definitions, applied business rules, data glossary and lineage
• Implement and maintain orchestration pipelines on Databricks and Airflow
• Develop solutions in Python and SQL for data transformation, cleansing and aggregation
• Implement data quality tests in dbt models and analytical layers
• Set up monitoring and alerts for pipelines and data models
• Lead complex end-to-end modeling projects
• Apply appropriate partitioning, clustering and materialization strategies to ensure performance and cost efficiency in data processing
• Collaborate with product and business squads

• Technical Leadership and Mentorship: Act as a reference for analysts and engineers, promoting development best practices, performing code reviews, and mentoring the team to advance analytical maturity.
• Strategic Data Modeling: Design and maintain robust data layers (Silver/Gold) in BigQuery, translating the industry's complex business rules into efficient dimensional models.
• Data Engineering Culture: Implement automated testing frameworks, data quality monitoring, and documentation via dbt, ensuring that security and reliability metrics are 100% trustworthy.
• Business Interface: Work proactively with Product Managers and functional areas to elicit requirements, investigate root causes of operational pain points, and propose architectural solutions that anticipate needs.
• Optimization, Scalability and FinOps: Identify bottlenecks in queries and pipelines, proposing refactors to improve performance and cost efficiency across the Group's large data volumes.

Analytics Engineer, Supply Chain

• Design and implement a multi-tier SaaS reporting infrastructure to support diverse client levels and brand campaign requirements.
• Lead the GA4 and Google Tag Manager strategy, overseeing implementation, QA, and the definition of event tracking standards for QR scans, lead captures, and promo engagement.
• Architect and manage GA4 data exports into BigQuery, building robust models that join event data with account, venue, and subscription datasets.
• Integrate data from diverse sources including Sanity CMS, Mailchimp, SMS platforms, and POS systems to create a unified view of the consumer journey.
• Develop reusable reporting templates and automated dashboards for client recaps, monthly summaries, and real-time campaign performance.
• Evaluate and deploy reporting automation tools such as Looker Studio Pro, AgencyAnalytics, or Whatagraph to enhance scalability.
• Establish data governance and access control protocols to ensure secure, account-specific reporting views.
• Partner with product and engineering teams to ensure tracking requirements are seamlessly integrated into the development lifecycle.
• Translate complex analytics into actionable business insights for internal stakeholders, venues, and brand partners.
• Maintain comprehensive documentation including tracking plans, data dictionaries, and architectural workflows.

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