Data Architect With DataBricks

• Design and implement enterprise-scale data architectures using Databricks Lakehouse Platform.
• Develop scalable data pipelines for batch and real-time data processing.
• Design and maintain Lakehouse architectures using Delta Lake.
• Define data modeling standards (conceptual, logical, and physical).
• Architect data ingestion frameworks using Auto Loader, Structured Streaming, and ETL/ELT best practices.
• Optimize Databricks workloads for performance, scalability, and cost efficiency.
• Implement data governance, metadata management, data quality, lineage, and security controls.
• Integrate Databricks with cloud-native services and enterprise applications.
• Collaborate with data engineering teams to establish CI/CD pipelines and DevOps best practices.
• Support data science and machine learning initiatives by designing feature-ready data platforms.
• Establish data architecture standards, reusable frameworks, and best practices.
• Evaluate emerging technologies and recommend architecture improvements.
• Ensure compliance with enterprise security, privacy, and regulatory requirements.

Databricks

• Strong hands-on experience with Databricks Lakehouse Platform
• Delta Lake
• Unity Catalog
• Databricks Workflows
• Databricks SQL
• Auto Loader
• Structured Streaming
• Delta Live Tables (DLT)
• MLflow (preferred)

Data Engineering

• Apache Spark (PySpark/Scala)
• Spark SQL
• ETL/ELT design
• Data Lake and Lakehouse architecture
• Data Warehousing concepts
• Data Modeling (Star Schema, Snowflake, Dimensional Modeling)
• Data Integration
• Batch and Streaming Architecture

Cloud Platforms (one or more)

• Microsoft Azure

• Azure Data Lake Storage (ADLS)

• Azure Data Factory (ADF)

• Azure Synapse Analytics

• Azure Key Vault

• Microsoft Entra ID (Azure AD)

Amazon Web Services (AWS)

• Amazon S3
• AWS Glue
• Amazon Redshift
• AWS IAM

Google Cloud Platform (Google Cloud Platform)

• Google Cloud Storage
• BigQuery
• Cloud Composer

Programming

• Python
• SQL
• Scala (preferred)
• Java (optional)

Database Technologies

• SQL Server
• Oracle
• PostgreSQL
• Snowflake
• MongoDB (preferred)

DevOps & Automation

• Git
• Azure DevOps or GitHub Actions
• Jenkins
• Terraform
• CI/CD Pipelines

Governance & Security

• Unity Catalog
• Data Governance
• Data Lineage
• Data Quality
• Role-Based Access Control (RBAC)
• Encryption and Security Best Practices, * Bachelor''s or Master''s degree in Computer Science, Information Technology, Data Engineering, or a related field.
• 8+ years of experience in Data Engineering or Data Architecture.
• 3+ years of hands-on experience with Databricks.
• Experience designing enterprise-scale cloud data platforms.
• Strong understanding of distributed data processing using Apache Spark.
• Experience with Lakehouse architecture and Delta Lake.
• Knowledge of data governance, metadata management, and security frameworks.
• Experience working in Agile/Scrum environments.