Data Engineer

🕒 Agosto 10

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🚰 Engenheiro de Dados

👻 Score fantasma 46%

O que significa: quão provável esta vaga está aberta sem contratação ativa.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

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v4c.ai

SiteLinkedInTodas as Vagas

51 - 200 funcionários

💼 Consultoria

🏥 Saúde

🏭 Manufatura

Consulting • Healthcare • Manufacturing

v4c. ai é uma empresa de serviços de dados e IA especializada em Databricks, que ajuda grandes empresas a modernizar suas plataformas de dados e acelerar suas iniciativas de IA. Oferecem serviços de consultoria, migração, MLOps, IA generativa e governança de dados, além de aceleradores de migração pré-construídos e pipelines de ML prontos para produção para reduzir o tempo de geração de valor. A v4c. ai atende indústrias reguladas e intensivas em dados (serviços financeiros, varejo, manufatura, saúde) e enfatiza a implementação do Databricks, implantação escalável de modelos e suporte operacional de longo prazo.

Descrição

• Design, develop, refine, and implement high-quality data architecture solutions for large-scale, complex data platforms • Establish best practices for modular, reusable, maintainable, and scalable data engineering code • Develop and maintain greenfield data engineering projects and production-level data pipelines • Design data-driven solutions for complex cybersecurity challenges • Architect end-to-end data platforms by evaluating technical approaches, trade-offs, risks, and performance requirements • Work hands-on with engineers to implement data architecture and pipeline solutions • Collaborate with DevOps and MLOps teams to monitor and optimize data pipelines and machine learning workflows • Improve model and pipeline performance through monitoring, troubleshooting, and tuning to meet latency and cost requirements • Evaluate third-party AI tools against internally developed solutions to maximize business value and ROI • Establish data governance, data quality, and security standards for ethical and compliant use of sensitive information in AI models • Develop incremental development strategies and production release plans aligned with long-term product vision • Serve as the data engineering subject matter expert for the AI team • Ensure data pipelines are reliable, scalable, secure, and optimized for production workloads

🎯 Requisitos

• Bachelor’s degree in Engineering, Computer Science, or a related technical field • At least 4 years of experience working with data platform solutions across data and analytics throughout the software development life cycle (SDLC) • At least 2 years of experience designing and mapping requirements into Databricks data pipeline capabilities on Google Cloud Platform (GCP) • Hands-on experience with Databricks Lakehouse architecture, including Databricks Datalake and Data Catalog capabilities • Experience designing Databricks Medallion Architecture, including data structure, data access, and data quality decisions • Strong experience with batch, mini-batch, and real-time data processing • Hands-on experience with ELT/ETL processes, data pipelines, and orchestration tools • Experience processing large volumes of unlabeled data with complex schemas • Strong understanding of data modeling techniques and modern data platform trends, including AI applications in data engineering • Proficiency in Python, Pandas, PySpark, and related data processing frameworks • Experience with AI and machine learning tools such as TensorFlow, PyTorch, Keras, LangChain, LangSmith, and Infrastructure as Code (IaC) • Experience working with structured and unstructured data formats, including Parquet, PostgreSQL, DuckDB, JSON, and PDF • Experience with infrastructure sizing and estimation for data platform solutions • Strong experience as an individual contributor with a hands-on engineering mindset and delivery focus • Advanced degree in Engineering, Computer Science, Machine Learning, or Artificial Intelligence is preferred • Preferred industry experience in cybersecurity, automotive, energy, or healthcare • Preferred experience in both large enterprises and small companies • Familiarity with supervised and unsupervised machine learning models • Experience with AI-driven data engineering solutions and integration of AI capabilities into data platforms • Knowledge of data governance, security, and compliance best practices • Excellent verbal and written communication skills • Strong collaboration, analytical thinking, problem-solving, self-management, prioritization, ownership, initiative, and independent-working abilities