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Azure Databricks Architect

Tata Consultancy Services

Hyderabad, Telangana, India (Hybrid) · Tempo total

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Experiência
8–10 anos
Salário
Vagas
1
Publicado
há 9 horas
Modo de trabalho
Híbrido
Educação
Qualquer graduado
Elegibilidade
Any graduate with 8 to 10 years of experience in cloud data architecture or Azure data engineering, particularly those who have built and supported Databricks and Azure-based data pipelines.
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Onde você trabalhará

Descrição da vaga

Role overview

Tata Consultancy Services is hiring an Azure Databricks Architect for a pan-India virtual engagement. The role calls for an experienced cloud data professional with 8 to 10 years of background in data architecture, Azure data services, and large-scale pipeline development.

Core expertise

The position requires strong working knowledge of data lake architecture along with hands-on experience across Azure services such as ADLS, Azure Data Factory, Azure Databricks, and Synapse. A solid grasp of lakehouse concepts, Databricks Delta, and Delta Live Tables is expected, along with practical exposure to SQL and PySpark/Python-based data processing.

Key technical work

  • Build and maintain ELT pipelines using Azure Data Factory and Databricks, including Autoloader-based ingestion, notebook-driven scripting, and Synapse activities such as Copy and Data Flow tasks.
  • Design metadata-driven pipelines with robust metadata management and dynamic processing logic.
  • Work with Azure Data Lake Storage and Azure Serverless SQL Pool for scalable data storage and querying.
  • Transform data using Spark and SQL while following cloud design patterns and established architecture best practices.
  • Use Git or comparable version control tools to manage code safely and consistently.
  • Investigate, troubleshoot, and resolve issues across ETL workflows and pipeline executions.
  • Understand Azure Databricks and Azure Synapse capabilities, internals, and feature sets in depth.
  • Support Azure DevOps-based CI/CD practices for streamlined delivery and deployment.
  • Apply data profiling, validation, cleansing, and quality checks to improve reliability of data outputs.

Additional expectations

Applicants should also be comfortable working with cloud-native solution patterns, debugging complex data flow issues, and contributing to reliable, maintainable enterprise data platforms.

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