Key Responsibilities: Design, architect, and implement scalable data platforms using Databricks , Apache Spark , and cloud-native services (preferably Azure or AWS ). Collaborate with cross-functional teams to define data strategies, data models, and architecture patterns aligned with business goals. Develop and optimize ELT/ETL pipelines, data lakes, and data warehouse solutions. Define and enforce data governance, security, and best practices for data ingestion, transformation, and analytics. Lead the modernization of legacy data systems and migrate workloads to Databricks-based platforms. Ensure high performance, availability, and scalability of data processing systems. Provide technical leadership and mentoring to data engineers and analysts. Work with product and business teams to translate data needs into architectural solutions. Required Skills & Experience: 12+ years of experience in Data Architecture , Data Engineering , or Analytics roles. Minimum 7 years of hands-on experience with Databricks , including Delta Lake, Spark SQL, MLflow, and Unity Catalog. Strong experience with big data technologies: Apache Spark , Kafka , Hive , Airflow , etc. Deep understanding of data modeling, dimensional modeling, and data warehousing concepts. Proficiency in Python , SQL , and Scala (preferred). Experience with cloud platforms like Azure , AWS , or GCP (Azure preferred). Familiarity with CI/CD , DevOps for data, and infrastructure-as-code. Strong understanding of data security , governance , and compliance standards . Excellent problem-solving, communication, and stakeholder management skills. Preferred Qualifications: Databricks Certification (e.g., Databricks Certified Data Engineer or Architect ) Experience working in regulated industries (e.g., finance, healthcare) Background in data science or machine learning integration is a plus