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