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About the role
Experience Required: 4+ Years Key Responsibility: Design, build, and maintain scalable and reliable data pipelines on Databricks, Snowflake, or equivalent cloud platforms. Ingest and process structured, semi-structured, and unstructured data from a variety of sources including APIs, RDBMS, and file systems. Perform data wrangling, cleansing, transformation, and enrichment using PySpark, Pandas, NumPy, or similar libraries. Optimize and manage large-scale data workflows for performance, scalability, and cost-efficiency. Write and optimize complex SQL queries for transformation, extraction, and reporting. Design and implement efficient data models and database schemas with appropriate partitioning and indexing strategies for Data Warehouse or Data Mart. Leverage cloud services (e.g., AWS S3, Glue, Kinesis, Lambda) for storage, processing, and orchestration. Build containerized solutions using Docker and manage deployment pipelines via CI/CD tools such as Azure DevOps, GitHub Actions, or Jenkins. Experience Required: 4+ Years Key Responsibility: Design, build, and maintain scalable and reliable data pipelines on Databricks, Snowflake, or equivalent cloud platforms. Ingest and process structured, semi-structured, and unstructured data from a variety of sources including APIs, RDBMS, and file systems. Perform data wrangling, cleansing, transformation, and enrichment using PySpark, Pandas, NumPy, or similar libraries. Optimize and manage large-scale data workflows for performance, scalability, and cost-efficiency. Write and optimize complex SQL queries for transformation, extraction, and reporting. Design and implement efficient data models and database schemas with appropriate partitioning and indexing strategies for Data Warehouse or Data Mart. Leverage cloud services (e.g., AWS S3, Glue, Kinesis, Lambda) for storage, processing, and orchestration. Build containerized solutions using Docker and manage deployment pipelines via CI/CD tools such as Azure DevOps, GitHub Actions, or Jenkins.
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