Data Scientist / Analytics Engineer

As a senior Databricks Architect, you will be responsible for designing, building, and governing the Lakehouse data platform. Your primary focus will be on driving the adoption of Databricks, Unity Catalog, and modern Lakehouse patterns across all data products and pipelines. Your key responsibilities will include: - Designing and implementing a production-grade Medallion Architecture (Bronze / Silver / Gold) across all data pipelines. - Defining data modeling standards and schema evolution policies across the Lakehouse. - Architecting end-to-end data flows from ingestion (streaming and batch) through transformation and serving layers. You will also lead the setup, configuration, and rollout of Unity Catalog as the centralized governance layer for all data assets. This includes implementing fine-grained access control, data masking policies, audit logging, data lineage tracking, and data classification frameworks. In addition, you will be responsible for building and maintaining production-grade data pipelines using PySpark, Delta Live Tables (DLT), and Databricks Workflows / Jobs. This will involve designing modular, reusable pipeline patterns, implementing robust pipeline observability, and leveraging Databricks Repos for CI/CD integration. Furthermore, you will optimize Spark execution plans, identify and resolve performance bottlenecks, and right-size cluster configurations to ensure efficient processing of large-scale distributed workloads. You will also set up and maintain Databricks Repos with standardized project structures, define Python coding standards, and build reusable Python utility libraries for common patterns. Qualifications Required: - 5+ years of hands-on experience with Databricks, with at least 2 years in an architect or senior lead role. - Deep expertise in Unity Catalog, Medallion Architecture, and Delta Lake. - Proven experience designing and deploying production pipelines with Databricks Jobs and Workflows. - Hands-on experience with Databricks Repos and CI/CD integration. - Experience configuring and operating Serverless SQL Warehouses and Serverless compute for Jobs. Additionally, you should have experience with DataFrames, Spark SQL, window functions, broadcast joins, UDFs, structured streaming, and micro-batch processing patterns. Advanced Python skills, familiarity with common data engineering libraries, cloud deployment experience, and relevant certifications are also required for this role. If interested, you will have the opportunity to partner with data engineers, data scientists, and analytics engineers to ensure the platform meets diverse workload needs, mentor the engineering team, and evaluate new Databricks features and third-party integrations relevant to the organization's data roadmap. As a senior Databricks Architect, you will be responsible for designing, building, and governing the Lakehouse data platform. Your primary focus will be on driving the adoption of Databricks, Unity Catalog, and modern Lakehouse patterns across all data products and pipelines. Your key responsibilities will include: - Designing and implementing a production-grade Medallion Architecture (Bronze / Silver / Gold) across all data pipelines. - Defining data modeling standards and schema evolution policies across the Lakehouse. - Architecting end-to-end data flows from ingestion (streaming and batch) through transformation and serving layers. You will also lead the setup, configuration, and rollout of Unity Catalog as the centralized governance layer for all data assets. This includes implementing fine-grained access control, data masking policies, audit logging, data lineage tracking, and data classification frameworks. In addition, you will be responsible for building and maintaining production-grade data pipelines using PySpark, Delta Live Tables (DLT), and Databricks Workflows / Jobs. This will involve designing modular, reusable pipeline patterns, implementing robust pipeline observability, and leveraging Databricks Repos for CI/CD integration. Furthermore, you will optimize Spark execution plans, identify and resolve performance bottlenecks, and right-size cluster configurations to ensure efficient processing of large-scale distributed workloads. You will also set up and maintain Databricks Repos with standardized project structures, define Python coding standards, and build reusable Python utility libraries for common patterns. Qualifications Required: - 5+ years of hands-on experience with Databricks, with at least 2 years in an architect or senior lead role. - Deep expertise in Unity Catalog, Medallion Architecture, and Delta Lake. - Proven experience designing and deploying production pipelines with Databricks Jobs and Workflows. - Hands-on experience with Databricks Repos and CI/CD integration. - Experience configuring and operating Serverless SQL Warehouses and Serverless compute for Jobs. Additionally, you should have experience with DataFrames, Spark SQL, window f