Senior Associate_Azure Data Enginer_Data and Analytics

• Design, develop, and optimize data pipelines and ETL processes using PySpark or Scala to extract, transform, and load large volumes of structured and unstructured data from diverse sources.
• Implement data ingestion, processing, and storage solutions on Azure cloud platform, leveraging services such as Azure Databricks, Azure Data Lake Storage, and Azure Synapse Analytics.
• Develop and maintain data models, schemas, and metadata to support efficient data access, query performance, and analytics requirements.
• Monitor pipeline performance, troubleshoot issues, and optimize data processing workflows for scalability, reliability, and costeffectiveness.
• Implement data security and compliance measures to protect sensitive information and ensure regulatory compliance.

• Proven experience as a Data Engineer, with expertise in building and optimizing data pipelines using PySpark, Scala, and Apache Spark.
• Handson experience with cloud platforms, particularly Azure, and proficiency in Azure services such as Azure Databricks, Azure Data Lake Storage, Azure Synapse Analytics, and Azure SQL Database.
• Strong programming skills in Python and Scala, with experience in software development, version control, and CI/CD practices.
• Familiarity with data warehousing concepts, dimensional modeling, and relational databases (eg, SQL Server, PostgreSQL, MySQL).
• Experience with big data technologies and frameworks (eg, Hadoop, Hive, HBase) is a plus.

Degrees/Field of Study required Master of Engineering, Bachelor of Engineering, Bachelor of Technology Degrees/Field of Study preferred Microsoft Azure, PySpark Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Hadoop, Azure Data Factory, Communication, Creativity, Data Anonymization, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake, Data Modeling, Data Pipeline, Data Quality, Data Transformation, Data Validation