• Design, develop, and maintain scalable data pipelines using Python, PySpark, and other modern programming languages to support both batch and streaming workloads • Build and optimize data processing frameworks on cloud platforms such as Databricks or Snowflake, ensuring performance, reliability, and cost efficiency • Design and implement robust data models, including transactional (OLTP) and dimensional (OLAP) schemas, to support analytics, reporting, and application integration • Develop high quality SQL code including complex queries, stored procedures, and views, with a focus on performance tuning and efficient data access patterns • Create and manage workflow orchestration using Apache Airflow or similar tools, ensuring reliable scheduling, dependency management, and monitoring • Implement and enforce data governance and metadata standards through tools such as Microsoft Purview, including data lineage, classification, cataloging, and security policies • Build automated data quality and validation frameworks to ensure accuracy, completeness, and reliability of production datasets • Collaborate with cross functional teams including data architects, analysts, scientists, and business stakeholders to understand requirements and deliver scalable, well designed data solutions • Lead technical design sessions and code reviews, promoting engineering best practices, reusability, and maintainability • Support cloud infrastructure and DevOps practices, including CI/CD pipelines, version control, testing automation, and environment management • Monitor and troubleshoot production data pipelines, proactively addressing issues, performance bottlenecks, and system failures • Contribute to the evolution of the enterprise data platform, recommending tools, frameworks, and architectures to improve scalability and efficiency