Data Engineer

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InVision Communications

SiteLinkedInTodas as Vagas

51 - 200 funcionários

Fundada em 1991

🤝 B2B

📱 Mídia

B2B • Media • Event Management

A InVision Communications é uma empresa focada em parcerias que se especializa na criação de experiências digitais e físicas envolventes que inspiram ações. Sua missão é proporcionar um engajamento autêntico através da combinação de estratégias de conteúdo eficazes e da tecnologia certa. Eles trabalham em estreita colaboração com seus clientes para aprimorar a interação do público e facilitar resultados exitosos em diversos eventos e campanhas, aproveitando sua vasta experiência para cultivar relacionamentos duradouros.

Descrição

• Inherit, evaluate, and take full ownership of existing ETL/ELT pipelines — identifying what to preserve, improve, or replace based on performance, reliability, and long-term maintainability. • Design and build scalable pipeline improvements or net-new solutions where current practices fall short. • Monitor pipeline health, troubleshoot data quality issues, and proactively resolve performance and reliability problems. • Manage and evolve orchestration tooling with openness to adopting better alternatives as infrastructure needs grow. • Optimize query performance, pipeline efficiency, and resource utilization across Convo’s data environment. • Participate in testing, deployment, and monitoring practices that promote long-term reliability and scalability. • Develop and maintain scalable data transformation processes, schema design, and data models that support evolving business requirements. • Establish and evolve data quality testing frameworks - building practices that catch issues early and create lasting internal trust in our data. • Own data governance, documentation, lineage, version control, and data quality standards across the organization. • Serve as the primary internal resource for data engineering guidance and recommendations, helping set standards and informing data infrastructure decisions across the organization. • Work closely with the data analyst to translate business questions into reliable, queryable data structures. • Educate and guide non-technical stakeholders on how to work effectively with data, what is and isn’t feasible, and how to frame data requests clearly. • Explore and implement tooling to enable self-service data discovery for internal teams, reducing bottlenecks and empowering stakeholders to answer their own questions. • Collaborate with Product, Engineering, Finance, Operations, and Data Science stakeholders to support reporting, forecasting, and business intelligence needs. • Partner with Product and Engineering teams to integrate analytics, event tracking, and reporting into products and platforms. • Establish and document data engineering standards, workflows, and best practices at Convo — building a foundation that is sustainable, well-understood, and not dependent on any single person. • Contribute to improvements in data architecture, tooling, monitoring, automation, and engineering best practices. • Evaluate emerging technologies and tooling to improve efficiency, automation, and accessibility of data systems. • Maintain clear technical documentation and operational standards that support long-term maintainability. • Exercise sound technical judgment in balancing immediate business needs with long-term platform sustainability. • Maintain strong confidentiality and discretion when handling sensitive organizational, financial, operational, and employee data.

🎯 Requisitos

• Strong SQL skills with hands-on experience in Snowflake and Snowflake SQL. • Proficiency in Python for data transformation, automation, and pipeline scripting. • Experience with dbt for data modeling and transformation. • Familiarity with git and version control best practices. • Solid understanding of ETL/ELT patterns, pipeline orchestration, and modern data modeling concepts. • Experience managing and supporting production-grade data infrastructure and pipelines. • Demonstrated ability to work independently, self-direct priorities, and make sound technical decisions without day-to-day oversight. • Experience troubleshooting data quality, reliability, and performance issues within complex data environments. • Ability to communicate technical concepts clearly and guide non-technical stakeholders on data capabilities and limitations. • A collaborative mindset and comfort working across teams with varying technical backgrounds. • Openness to inheriting existing systems and the judgment to know when to improve versus rebuild. • Openness to learning new tools and technologies as the data engineering landscape continues to evolve. • Ability to handle sensitive and confidential information with strong integrity and professionalism.

🏖️ Benefícios

• Health insurance • 401(k) matching • Flexible work hours • Paid time off