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Open nowPosted 47 hours ago

Dev Analytics Engineer / Data Analyst

Moot It Consulting

Where
São Paulo, SP, Brasil
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Your applicationOpen nowDev Analytics Engineer / Data AnalystMoot 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.

11.9% 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. 4.4%3 days
  3. 11.9%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 47 hours 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) Analytics Engineer / Data Analyst para o programa de migração e modernização do DW legado para o Enterprise Lakehouse Databricks, com a missão de reapontar 100% do consumo — dashboards de BI, relatórios, aplicações e integrações sistêmicas — para os domínios da camada gold. ​

Requisitos necessários:

• Analytics Engineering: experiência sólida em camada de consumo — modelagem dimensional/marts, camada semântica e métricas certificadas. • Databricks: Spark SQL em produção, Delta Lake e arquitetura medalhão (bronze, silver, gold). • Reapontamento de BI (Power BI, Tableau ou similar): troca de fontes, refatoração de datasets e validação de paridade de números. • Integrações sistêmicas de dados: APIs, extrações batch e cargas entre sistemas, incluindo contratos de interface e planos de rollback. • SQL avançado: técnicas de reconciliação e comparação de bases em escala. • Unity Catalog (lineage, descoberta e documentação de métricas e modelos de consumo). • Modelagem dimensional (Kimball/star schema) e boas práticas de marts por domínio. • Python e PySpark para automação de validações de paridade e rotinas de reapontamento. • Data Contracts e Data Quality (expectations, DQ gates) aplicados à camada de consumo.​

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