Databricks Data Architect

As a Data Engineer/Architect, you will be responsible for designing and implementing data engineering solutions to support the organization's data architecture. Your qualifications should include: - Bachelors/Masters degree in Computer Science, Engineering, or related field - 10+ years of experience in data engineering/architecture - 4+ years of hands-on Databricks experience - Strong expertise in PySpark, SQL, Python, Delta Lake and Lakehouse architecture, Distributed data processing In this role, your key responsibilities will include: - Hands-on Knowledge of Databricks, including Delta Lake, Unity Catalog, Lakeflow, Lakebase, Databricks Apps, Workflows, job scheduling, ETL/ ELT pipeline orchestration, and more - Strong understanding of Databricks architecture, such as control plane vs data plane, workspace model - Experience with Cluster sizing, autoscaling, workload-based optimization, Cost estimation, performance tuning, and FinOps - Building CI/CD pipelines and Declarative Automation Bundle (DAB) implementation - Exposure to AI/ML/LLM integrations within the Databricks ecosystem like MLflow, external LLM APIs - Understanding of cloud landing zone concepts, subscription design, governance, policies - Understanding of enterprise security architecture for data platforms - Cloud platforms: Azure/AWS/GCP - Data modeling: Dimensional, Data Vault, Data warehousing - API and data integration patterns Good-to-Have skills include: - Understanding of cloud network topology for secure data platforms, VNet injection, private endpoints, subnet isolation, NSG, Secure access to storage, Databricks, and external systems - Knowledge of hybrid connectivity models, On-premises integration (VPN, ExpressRoute), Cross-cloud architecture (AWS/Azure/GCP interoperability) - Identity and access management, Secret Management - Knowledge and Hands-on experience with Data governance tools like Collibra, Alation, or equivalent - Real-time streaming design and implementation As a Data Engineer/Architect, you will be responsible for designing and implementing data engineering solutions to support the organization's data architecture. Your qualifications should include: - Bachelors/Masters degree in Computer Science, Engineering, or related field - 10+ years of experience in data engineering/architecture - 4+ years of hands-on Databricks experience - Strong expertise in PySpark, SQL, Python, Delta Lake and Lakehouse architecture, Distributed data processing In this role, your key responsibilities will include: - Hands-on Knowledge of Databricks, including Delta Lake, Unity Catalog, Lakeflow, Lakebase, Databricks Apps, Workflows, job scheduling, ETL/ ELT pipeline orchestration, and more - Strong understanding of Databricks architecture, such as control plane vs data plane, workspace model - Experience with Cluster sizing, autoscaling, workload-based optimization, Cost estimation, performance tuning, and FinOps - Building CI/CD pipelines and Declarative Automation Bundle (DAB) implementation - Exposure to AI/ML/LLM integrations within the Databricks ecosystem like MLflow, external LLM APIs - Understanding of cloud landing zone concepts, subscription design, governance, policies - Understanding of enterprise security architecture for data platforms - Cloud platforms: Azure/AWS/GCP - Data modeling: Dimensional, Data Vault, Data warehousing - API and data integration patterns Good-to-Have skills include: - Understanding of cloud network topology for secure data platforms, VNet injection, private endpoints, subnet isolation, NSG, Secure access to storage, Databricks, and external systems - Knowledge of hybrid connectivity models, On-premises integration (VPN, ExpressRoute), Cross-cloud architecture (AWS/Azure/GCP interoperability) - Identity and access management, Secret Management - Knowledge and Hands-on experience with Data governance tools like Collibra, Alation, or equivalent - Real-time streaming design and implementation