• Lead product strategy for SurveyMonkey’s agentic and conversational surface areas. Influencing how intelligent, automated workflows change what it means to run a survey program. • Own the AI/ML platform layer, enabling AI capabilities across the product, in close partnership with LLM infrastructure and data science teams. • Define and execute a multi-phase strategy to make our data corpus a customer-facing competitive advantage, from cross-survey intelligence to industry benchmarks to predictive signals. • Lead a team of PMs focused on AI platform and data products

Role Description Projeto Temporário - 160 horas de Projeto. Freelancer em RPA (recibo de pagamento para autônomo). Buscamos um(a) Engenheiro(a) de Dados Sênior, com perfil hands-on, para atuar no desenvolvimento e evolução de soluções modernas de dados e Inteligência Artificial, utilizando Databricks como plataforma principal. Procuramos um profissional com forte capacidade técnica para projetar, desenvolver e otimizar pipelines de dados, além de atuar na construção de soluções de IA Generativa, com destaque para o uso do Databricks Genie. - Desenvolver e manter pipelines de dados escaláveis utilizando Databricks, Spark e PySpark. - Projetar soluções de ETL/ELT para ambientes de alta volumetria. - Desenvolver notebooks e workflows em Databricks com foco em performance, qualidade e governança. - Atuar na implementação de arquiteturas modernas de dados utilizando Unity Catalog, Delta Live Tables e demais recursos da plataforma Databricks. - Criar e evoluir soluções baseadas em IA Generativa utilizando Databricks Genie, Mosaic AI e integração com LLMs. - Desenvolver APIs e automações em Python para integração entre plataformas e soluções analíticas. - Atuar na modelagem, catalogação e governança de dados. - Participar da definição de arquitetura, padrões técnicos e boas práticas de Engenharia de Dados. - Trabalhar em conjunto com arquitetos, cientistas de dados e áreas de negócio na construção de soluções analíticas. Qualifications - Experiência sólida como Engenheiro(a) de Dados. - Perfil hands-on, com atuação direta no desenvolvimento de soluções. - Experiência avançada em: - Databricks; - Apache Spark / PySpark; - Python; - SQL; - ETL/ELT; - Data Lake e Data Lakehouse. - Vivência com ambientes Cloud (AWS, Azure, GCP ou OCI). - Experiência com versionamento de código, CI/CD e DevOps. - Conhecimento em modelagem de dados, governança e otimização de performance. Requirements - Experiência prática com Databricks Genie (Genie Spaces), Mosaic AI e desenvolvimento de soluções utilizando IA Generativa e LLMs. - Experiência com Unity Catalog e Delta Live Tables. - Vivência em desenvolvimento de APIs (FastAPI ou similares). - Experiência em Analytics Avançado e BI. - Certificações Databricks. - Ter atuado em projetos do segmento de energia (Utilities/Oil & Gas/Distribuição de Energia/Gás). Competências - Perfil analítico e orientado à solução de problemas. - Facilidade para atuar em ambientes colaborativos. - Proatividade e autonomia. - Boa comunicação e capacidade de interação com times multidisciplinares. - Foco em qualidade, performance e inovação.

• Design, develop, and maintain scalable data pipelines and transformation workflows within Snowflake. • Build and optimize ELT/ETL processes using SQL, Python, Snowpark, dbt, and Snowflake-native capabilities. • Modernize existing Python- and stored-procedure-based pipelines using set-based processing, incremental loading, and reusable engineering patterns. • Implement Snowflake-native data-processing solutions using Dynamic Tables, Streams, Tasks, and stored procedures. • Improve platform performance and cost efficiency through query tuning, warehouse right-sizing, clustering strategies, workload isolation, and auto-suspend and auto-resume policies. • Develop reliable orchestration, dependency management, monitoring, error handling, retry, and recovery mechanisms. • Design and maintain dimensional, relational, and domain-oriented data models. • Implement data-quality checks, observability, testing, lineage, and documentation standards. • Integrate data from batch and real-time sources using tools such as Fivetran, Kafka, Airflow, or similar technologies. • Contribute to CI/CD pipelines, infrastructure-as-code practices, and automated deployment processes. • Work closely with architects and client stakeholders to translate business and technical requirements into production-ready solutions. • Participate in code reviews, technical design discussions, estimation, and Agile delivery activities.

• Lead the architecture, assessment, and modernization of enterprise Snowflake data platforms. • Define current-state and target-state architectures across data ingestion, transformation, storage, orchestration, governance, security, and consumption. • Lead inventories and assessments of Python-, SQL-, and stored-procedure-based pipelines, classifying workloads by business criticality, frequency, volume, runtime, reliability, and cost. • Analyze Snowflake usage telemetry to establish cost and performance baselines across pipelines, warehouses, and workloads. • Identify architectural and engineering anti-patterns, including procedural row-by-row processing, unnecessary full refreshes, missing incremental patterns, redundant transformations, warehouse mis-sizing, and insufficient workload isolation. • Recommend modernization strategies using set-based processing, incremental data patterns, Dynamic Tables, Streams and Tasks, Snowpark, dbt, and appropriate orchestration technologies. • Design scalable data models, governed data domains, semantic layers, and context models that support analytics and AI use cases. • Architect Snowflake Cortex solutions using capabilities such as Cortex Analyst, Cortex Search, and Cortex LLM functions. • Establish architectural standards for security, access control, data governance, lineage, observability, performance, resilience, and business continuity. • Design data lakehouse and real-time data-processing architectures across AWS, Azure, or GCP. • Evaluate and recommend appropriate use of Snowflake Iceberg Tables and other open data architecture patterns. • Build prioritized optimization backlogs and sequenced delivery roadmaps based on business impact, technical risk, cost, performance, and implementation effort. • Provide technical leadership across multiple concurrent projects and guide engineers, analysts, QA specialists, and DevOps teams. • Support project planning, estimation, scope definition, dependency management, risk mitigation, and technical governance. • Lead discovery workshops and interviews with client stakeholders and subject-matter experts. • Present architecture decisions, assessment findings, quantified impact projections, and roadmap recommendations to technical and executive audiences. • Act as a trusted advisor to clients throughout architecture and delivery engagements. • Create and maintain architecture diagrams, decision records, technical standards, and platform documentation.