Azure Databricks Architect
Hyderabad, Telangana, India (Hybrid) · Full Time
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- Experience
- 8–10 yrs
- Salary
- —
- Openings
- 1
- Posted
- 3 hours ago
- Work mode
- Hybrid
- Education
- Any graduate
- Eligibility
- 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.
- Resume
- Required to apply
Where you'll work
Job description
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.