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Open nowPosted 4 days ago

Engenheiro(a) de Dados

Moot It Consulting

Where
São Paulo, SP, Brasil
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Your applicationOpen nowEngenheiro(a) de DadosMoot It Consulting · São Paulo, SP, Brasil

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Moot It Consulting's own form.

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 4 days ago

The posting

Somos umaconsultoria com mais de 12 anos de experiência em TI, Inovação, Otimização deProcessos, Softwares de Gestão, Desenvolvimento e Suporte a Sistemas, e Recrutamento de Profissionais de TI. Comprometidos com qualidade e inovação,oferecemos projetos de tecnologia sob medida, atendendo comexcelência todo o Brasil.

​Buscamos um(a) Engenheiro(a) de Dados para o programa de migração do DW legado para o Enterprise Lakehouse Databricks, com a missão de reconstruir os pipelines hoje implementados em DataStage e Azure Synapse — sem lift-and-shift. A partir da engenharia reversa dos jobs atuais. Será responsável por reimplementar os pipelines nativamente em PySpark e Spark SQL sobre a arquitetura medalhão (bronze/silver/gold), executando as waves de reconstrução por domínio com reconciliação e paridade contra o DW legado até o cutover.​

Requisitos necessários:​

​• PySpark e Python avançados: desenvolvimento de pipelines batch em escala, com testes e boas práticas de engenharia. ​• SQL avançado e modelagem de dados: transformações complexas, tuning de performance e modelagem relacional/dimensional em contexto de DW. ​• Databricks e Delta Lake: jobs, workflows e arquitetura medalhão (bronze, silver, gold) em produção; Delta Live Tables / Lakeflow e Asset Bundles. ​• Migração / reconstrução de pipelines ETL: tradução de regras de negócio de ferramentas legadas para Spark (sem lift-and-shift), com ingestão CDC/batch de bancos relacionais. ​• Ecossistema AWS: S3, Glue, EMR, Athena, Lambda, DMS e Step Functions. ​• Azure Synapse: experiência com o ambiente legado (SQL pools e pipelines) para apoiar a engenharia reversa. ​• Qualidade e reconciliação de dados: testes automatizados, DQ gates e comparação legado × novo para aceite de paridade. ​• Git e CI/CD aplicados a pipelines de dados.​

  • Atuação: Hibrido 2 dias pressenciais - SP
  • Formação: Graduação Concluída
  • Idioma : Inglês Avançado
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