Databricks Unified Data Analytics Platform

As a Data Platform Architect, you will play a crucial role in architecting the data platform blueprint, implementing designs, and ensuring cohesive integration between systems and data models. Your responsibilities will include leading enterprise-scale Lakehouse initiatives, designing and building complex pipelines using PySpark and Delta Lake, architecting data ingestion and transformation processes, and enabling cross-platform data access. Key Responsibilities: - Lead enterprise-scale Lakehouse initiatives and architect modern Data & Analytics Architecture patterns. - Design and build complex pipelines using PySpark for ETL/ELT processes. - Architect data ingestion and transformation using DLT Expectations and modular Databricks Functions. - Implement Unity Catalog for organization-wide governance and data access. - Integrate Databricks Marketplace for consuming third-party data and publishing internal data assets. - Govern and manage Delta Sharing for securely sharing datasets with external partners. - Design and maintain PII anonymization and masking strategies for GDPR/HIPAA compliance. - Architect Power BI, Tableau, and Looker integration with Databricks for live reporting and visualization. - Build Databricks SQL Dashboards for real-time insights and KPI tracking. - Lead cross-functional initiatives to deliver secure, scalable, and value-aligned data products. - Provide thought leadership on adopting advanced features like Mosaic AI and Model Serving. Qualifications Required: - Minimum 12 years of experience as a Data Architect and Data Engineer. - Expert-level understanding of modern Data & Analytics Architecture patterns. - Strong programming and debugging skills in Python. - Hands-on expertise in at least one major cloud platform: AWS, GCP, or Azure. - Experience with cloud-based services relevant to data engineering, data storage, and data processing. - Strong background in data modeling and data warehousing concepts. Additional Details: - Certifications such as Databricks Certified Professional are a plus. - Knowledge of machine learning concepts and big data processing tools is beneficial. - Familiarity with ETL tools like Informatica, Talend, or Matillion is advantageous. - Working knowledge of DBT (Data Build Tool) and CI/CD pipelines in a cloud environment is preferred. Please note that 15 years of full-time education is required for this role. As a Data Platform Architect, you will play a crucial role in architecting the data platform blueprint, implementing designs, and ensuring cohesive integration between systems and data models. Your responsibilities will include leading enterprise-scale Lakehouse initiatives, designing and building complex pipelines using PySpark and Delta Lake, architecting data ingestion and transformation processes, and enabling cross-platform data access. Key Responsibilities: - Lead enterprise-scale Lakehouse initiatives and architect modern Data & Analytics Architecture patterns. - Design and build complex pipelines using PySpark for ETL/ELT processes. - Architect data ingestion and transformation using DLT Expectations and modular Databricks Functions. - Implement Unity Catalog for organization-wide governance and data access. - Integrate Databricks Marketplace for consuming third-party data and publishing internal data assets. - Govern and manage Delta Sharing for securely sharing datasets with external partners. - Design and maintain PII anonymization and masking strategies for GDPR/HIPAA compliance. - Architect Power BI, Tableau, and Looker integration with Databricks for live reporting and visualization. - Build Databricks SQL Dashboards for real-time insights and KPI tracking. - Lead cross-functional initiatives to deliver secure, scalable, and value-aligned data products. - Provide thought leadership on adopting advanced features like Mosaic AI and Model Serving. Qualifications Required: - Minimum 12 years of experience as a Data Architect and Data Engineer. - Expert-level understanding of modern Data & Analytics Architecture patterns. - Strong programming and debugging skills in Python. - Hands-on expertise in at least one major cloud platform: AWS, GCP, or Azure. - Experience with cloud-based services relevant to data engineering, data storage, and data processing. - Strong background in data modeling and data warehousing concepts. Additional Details: - Certifications such as Databricks Certified Professional are a plus. - Knowledge of machine learning concepts and big data processing tools is beneficial. - Familiarity with ETL tools like Informatica, Talend, or Matillion is advantageous. - Working knowledge of DBT (Data Build Tool) and CI/CD pipelines in a cloud environment is preferred. Please note that 15 years of full-time education is required for this role.