Skills required: Must have experience with: Analyze business requirements, perform data analysis, and develop technical design documents in collaboration with product and data teams; Design and build scalable, distributed data pipelines/ETL solutions using Spark, Hadoop, Hive, Azure Databricks, Azure Data Factory, Azure Blob Storage, GCP Dataproc, and BigQuery; Develop high-quality data solutions using object-oriented programming (Python, Scala) following industry best practices; Automate workflows and streamline processes using Python and shell scripting; build lightweight ETL frameworks; Optimize and tune Spark applications (Spark Core, Spark SQL, Spark Streaming) to improve performance and reduce costs; Implement data pipelines on cloud platforms including Microsoft Azure, Databricks, Google Cloud Platform and BigQuery; Orchestrate workflows using tools such as Apache Airflow, Azure Data Factory, GCP Cloud Composer, Automic, and Oozie; Establish CI/CD pipelines using GitHub, Jenkins, JFrog, and Artifactory; Manage code with Git-based version control, enabling collaboration, code review, and release management; Write optimized SQL for data extraction, transformation, and warehousing (Hive, BigQuery); Collaborate with cross-functional teams to deliver scalable data solutions on time; Apply Agile and Waterfall methodologies based on project needs; Design data models across RDBMS and cloud systems to support efficient data access and analytics. Minimum education and experience required: Master’s degree or the equivalent in Computer Science, Engineering (any) or a related field; OR Bachelor’s degree or the equivalent in Computer Science, Engineering (any) or a related field plus 2 years of experience in software engineering or related experience.