Cortex

Public Relations

• Commercial strategy development: Create and implement strategic sales plans aligned with company objectives; evolve the sales model from volume-based to Account-Based Sales (ABS).
• Build and execute the enterprise new-business acquisition strategy.
• Define operating priorities based on territory, ICP, and account propensity.
• Team management: Lead a high-performance team, providing guidance and ongoing support to ensure the professional development of team members.
• Sales pipeline management: Manage the full sales cycle from prospecting to closing, ensuring a healthy and predictable pipeline.
• Ensure healthy pipeline generation from multiple sources: Outbound, Inbound, Sales-led, Events, and Relationship-based channels.
• Manage the pipeline with a focus on: Quality (advanced pipeline) and Predictability (forecast). Monitor stage-by-stage conversion and analyze each performance indicator.
• Performance analysis: Monitor and analyze sales performance metrics to evaluate progress against targets and implement improvements when necessary.
• Client relationships: Build and maintain strong relationships with key clients and business partners to ensure customer satisfaction and loyalty.
• Market analysis: Monitor market trends, identify new business opportunities, and adjust strategies as needed.
• Market intelligence and positioning: Monitor trends in corporate communication, PR intelligence, reputation, and media. Contribute to refining positioning and the value proposition. Identify opportunities for market expansion and new use cases.
• Collaboration with other teams: Work closely with Marketing (demand generation and ABM), Product (solution evolution), and Customer Success (expansion and retention). Act in strong partnership with cross-functional stakeholders.
• Reporting and analysis: Prepare regular reports on sales performance, providing insights into performance metrics and recommending process improvements for the team.

Sales Manager – Cortex Brand

Engineer

• Develop and maintain high-performance, resilient crawlers/bots for large-scale data extraction.
• Design and implement complex data pipelines using Databricks (Spark) for batch and streaming processing.
• Ensure the health and reliability of data flows using advanced orchestration tools.
• Manage and optimize resources within the AWS ecosystem to ensure scalability and cost efficiency.
• Implement error-handling techniques, block-workarounds (proxies, captchas) and data quality validation for collected data.

Senior Data Engineer – Crawlers & Orchestration Specialist

Human Resources

• Act as a strategic partner to leadership, supporting people-related decisions aligned with business goals.
• Oversee the employee lifecycle (onboarding, engagement, development, and retention), ensuring a positive experience and alignment with company culture.
• Analyze people metrics (turnover, engagement, performance) and generate insights to support decision-making.
• Monitor pulse and engagement surveys, supporting leaders in developing and executing action plans.
• People Analytics (data collection and analysis).
• Lead and support performance management cycles (checkpoints, calibration, feedback, and Individual Development Plans - IDPs).
• Support leaders in team development, strengthening management practices and career development.
• Conduct training and leadership development and succession planning initiatives.
• Work on strategic People projects, proposing process improvements and business-oriented solutions.

HR Business Partner – HRBP

Customer Success Manager

• Lead the Customer Success team responsible for mid-market and high-volume accounts, ensuring a scalable, efficient, and value-driven customer journey;
• Build and maintain strategic relationships with sponsors and client leadership across your portfolio, managing an executive engagement cadence (QBRs, check-ins, business reviews, etc.);
• Map, implement, and evolve tools and integrations that support automation of the customer journey (automated emails, in-app flows, alerts, health scores, etc.);
• Analyze key metrics (NRR, churn, adoption, engagement, feature usage) and adjust strategies with a focus on efficiency and impact at scale;
• Design segmentation and mass-personalization strategies based on behavioral and profile data;
• Partner with Marketing, Sales, Product, and Support to ensure consistency and smoothness in the customer experience across the entire journey;
• Lead team performance rituals focused on productivity, operational efficiency, and retention outcomes;
• Contribute strategic vision for the sustainable growth of the customer base by designing mechanisms for scale and continuous engagement.

• Proactively monitor and manage client health and the relevance of projects to the client’s business
• Understand the product and ecosystem and identify alternatives to improve project relevance and adoption
• Study the industry and the specific characteristics of each client’s business
• Act as the client’s voice to other teams within the company, sharing pain points and seeking solutions internally
• Ensure delivery and compliance with deadlines agreed with the client

Customer Success Intern – Brand

Data Scientist

• Development of machine learning models;
• Data preparation, exploratory analysis and data visualization;
• Model and hypothesis validation;
• Python programming and code versioning;
• Communication with internal and external stakeholders;

Data Engineer

• Desenvolver, manter e otimizar pipelines de dados utilizando Apache Spark.
• Implementar e gerenciar soluções de dados na plataforma Databricks.
• Implementar e gerenciar soluções de orquestração de pipeline de dados na plataforma Apache Airflow.
• Projetar e implementar arquitetura de dados na AWS, utilizando serviços como S3, RDS, DynamoDB, ECS, EKS, Glue, Lambda, entre outros.
• Garantir a qualidade e integridade dos dados através de práticas de validação e monitoramento.
• Colaborar com equipes de análise de dados, ciência de dados e outros stakeholders para entender necessidades de dados e fornecer soluções eficazes.
• Participar na definição e implementação de boas práticas de engenharia de dados.
• Documentar processos, pipelines e arquitetura de dados de forma clara e detalhada.