Data Engineer (PySpark&Data Modeling)

<div><p>We are looking for a skilled<b>Data Engineer</b>with strong expertise in<b>PySpark and Data Modeling</b>to join our Data&Analytics team. The ideal candidate will be responsible for building scalable data pipelines, optimizing data workflows, and supporting advanced analytics initiatives.</p><h3><b>Key Responsibilities</b></h3><ul><li>Design, develop, and maintain scalable<b>data pipelines using PySpark</b></li><li>Perform<b>data modeling</b>(conceptual, logical, and physical) for analytics and reporting</li><li>Build and optimize<b>ETL/ELT workflows</b>for large-scale datasets</li><li>Work with structured and unstructured data across multiple sources</li><li>Ensure data quality, integrity, and governance standards</li><li>Collaborate with data analysts, scientists, and business stakeholders</li><li>Optimize performance of Spark jobs and data processing systems</li><li>Support deployment and monitoring of data solutions in production</li></ul><h3><b>Required Skills&Qualifications</b></h3><ul><li>Strong experience in<b>PySpark and Apache Spark ecosystem</b></li><li>Hands-on experience in<b>data modeling (Star Schema, Snowflake, etc.)</b></li><li>Proficiency in<b>SQL and database technologies</b></li><li>Experience with<b>data warehousing concepts</b></li><li>Knowledge of<b>ETL/ELT tools and frameworks</b></li><li>Familiarity with<b>cloud platforms (AWS / Azure / GCP)</b>is a plus</li><li>Understanding of<b>big data technologies (Hadoop, Hive, Kafka, etc.)</b></li><li>Strong problem-solving and analytical skills</li></ul><h3><b>Preferred Qualifications</b></h3><ul><li>Experience in<b>banking/financial services domain</b></li><li>Exposure to<b>data governance and data quality frameworks</b></li><li>Knowledge of<b>CI/CD pipelines and DevOps practices</b></li></ul></div>#J-18808-Ljbffr