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Qualification: Bachelor Of Engineering\n\nDesired Competencies\n(Technical/Behavioral Competency)\n\nMust-Have\n\nStrong\nhands-on experience in Python and PySpark for data processing and data\nengineering activities. \nExperience in developing data solutions using PySpark, Spark SQL, and\nrelated frameworks/libraries. \nHands-on experience in building and maintaining ETL/ELT pipelines, data\ningestion pipelines, and data transformation processes. \nExperience in ingesting data from multiple sources such as databases,\nfiles, cloud storage, APIs, S3/data lake platforms, or similar. \nExperience working with structured and unstructured data. \nGood understanding of data warehouse concepts, data lake concepts, and\ndata processing patterns. \nAbility to develop scalable, reusable, and maintainable data processing\ncomponents. \nExperience in end-to-end data pipeline development, including source\ningestion, transformation, validation, and target load. \nGood knowledge of SQL for data analysis, transformation, validation,\nand basic performance tuning. \nAbility to write clean, efficient, reusable, and scalable Python code.\nGood understanding of data quality checks, testing, monitoring,\ndocumentation, and production support practices. \nAwareness of security and data protection principles in data\nengineering solutions.\n\nGood-to-Have\n\nExposure to cloud platforms such as AWS,\nAzure, or GCP and related data services. \nExperience with cloud storage/data platforms such as S3, ADLS, Blob\nStorage, Databricks, EMR, Synapse, or similar. \nKnowledge of orchestration/scheduling tools such as Airflow, Control-M,\nAzure Data Factory, AWS Glue, Oozie, or similar. \nExposure to CI/CD, automation, pipeline deployment, and monitoring\nconcepts. \nKnowledge of data management principles, metadata management, data\ngovernance, and data lineage. \nExperience with Python frameworks such as Flask, Django, or FastAPI\nwill be an added advantage. \nKnowledge of ORM concepts will 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