Key Responsibilities: Design, develop, and maintain enterprise-grade data architecture and pipelines. Architect and optimize data lakehouse solutions using Databricks on Azure. Implement secure, efficient, and scalable data integration patterns using Snowflake and Azure Data Services (ADF, Data Lake, Synapse, etc.). Collaborate with cross-functional teams (Data Engineers, Analysts, DevOps, Product Owners) to gather requirements and deliver end-to-end data solutions. Ensure data quality, governance, and compliance across all solutions. Develop data models and optimize storage for performance and cost efficiency. Create automation scripts and frameworks using Python and Spark for data processing. Conduct performance tuning and troubleshooting of data pipelines and queries. Evaluate and integrate new tools and technologies to continuously improve the data architecture. Must-Have Skills: 14+ years of experience in data architecture, data engineering, or data platform design. Expert-level hands-on experience with Databricks, Snowflake, and Azure Data Services. Strong command of Python and PySpark for ETL/ELT processes. Proficient in SQL and performance tuning for complex data sets. Deep understanding of big data architecture, distributed computing, and data lakehouse patterns. Experience in building and managing CI/CD pipelines and infrastructure-as-code for data platforms. Strong problem-solving and communication skills. Preferred Qualifications: Azure certifications (e.g., Azure Data Engineer Associate, Azure Solutions Architect) Experience with tools such as Airflow, Terraform, dbt, or Power BI