Consultor de Dados – Engenharia & Ciência de Dados
New
J
JobgetherData Engineering & Science
BrazilContract
Salary not disclosed
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Job Details
- Required Skills
- Data engineeringData scienceData modelingDatabricks
Requirements
- Experience working across both Data Engineering and Data Science, with the ability to operate in a hybrid technical role.
- Practical experience with Microsoft Azure data services, particularly Azure Data Lake, Synapse, Databricks, or comparable technologies.
- Experience with conceptual, logical, and physical data modeling.
- Proven experience designing and developing data pipelines.
- Experience structuring data environments covering data ingestion, transformation, organization, and layered architectures.
- Strong analytical capabilities and the ability to understand business requirements and translate them into technical data solutions.
- Business-oriented mindset with a focus on using data and technology to generate operational value.
- Strong problem-solving skills and attention to data quality, organization, and availability.
- Ability to collaborate effectively with technical teams and business stakeholders.
- Adaptability, autonomy, and willingness to continuously develop technical expertise.
Responsibilities
- Design, model, structure, and evolve data environments within Microsoft Azure.
- Build and maintain scalable data pipelines supporting ingestion, transformation, organization, and availability of data.
- Develop and optimize data architectures using Azure Data Lake, Synapse, Databricks, and related services.
- Structure data environments using appropriate layers and architectures to improve organization, quality, and accessibility.
- Apply conceptual, logical, and physical data modeling practices to support robust data solutions.
- Analyze business requirements and translate operational needs into effective, data-driven technical solutions.
- Ensure data environments support analytical use cases and generate actionable value for business operations.
- Contribute to the continuous improvement, reliability, scalability, and performance of data platforms.
- Work across engineering and data science disciplines to connect technical implementation with analytical objectives.
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