Job Title: Data Engineer (PySpark) We are seeking a highly skilled  Senior Data Engineer  with strong expertise in  PySpark  and  data modelling  to join our Digital Platforms delivery team.
The ideal candidate will design, build, and optimize scalable data pipelines and contribute to enterprise-grade data architecture supporting analytics, reporting, and digital banking initiatives.
Key Responsibilities Design, develop, and maintain scalable  data pipelines  using PySpark and distributed data processing frameworks Build and optimize  ETL/ELT workflows  for large-scale structured and unstructured datasets Implement robust  data models  (dimensional, relational, and data vault) aligned with business requirements Work closely with stakeholders, data analysts, and business teams to translate requirements into technical solutions Ensure  data quality, governance, and consistency  across multiple data sources Optimize performance of Spark jobs and handle large datasets efficiently Collaborate with DevOps teams for deployment, CI/CD, and monitoring of data pipelines Troubleshoot data issues and ensure high availability and reliability of data systems Contribute to data platform architecture and best practices within the Data Engineering chapter Required Skills & Experience 5–8+ years of experience in  data engineering  or related roles Strong hands-on experience with  PySpark / Apache Spark Expertise in  data modelling  (Star Schema, Snowflake, Data Vault) Experience with  big data ecosystems  (Hadoop, Hive, Spark, Kafka – preferred) Proficiency in  Python  and SQL Experience with  cloud platforms  (AWS / Azure / GCP – any one preferred) Strong understanding of  ETL frameworks and data warehousing concepts Experience working in  agile environments Good to Have Experience in  banking or financial services domain Knowledge of  real-time data processing / streaming (Kafka, Spark Streaming) Exposure to  data governance and security practices Familiarity with tools like Airflow, Databricks, or similar platforms