Data Engineer

• Develop data ingestion and transformation pipelines using Databricks, PySpark, Python and SQL.
• Integrate data from multiple sources and prepare datasets for reporting, analytics and machine-learning use cases.
• Build reusable transformation components and analytical data models.
• Implement data-quality checks, reconciliation, exception handling and pipeline monitoring.
• Optimize Spark workloads for performance, reliability and cost.
• Apply data security, access controls, lineage and documentation standards.
• Support automated deployment, production troubleshooting and ongoing pipeline maintenance.
• Translate banking business requirements into sustainable data solutions.

Required Skills & Experience

• Strong hands-on experience with Databricks and PySpark
• Proficiency in Python and SQL
• Experience building ETL/ELT pipelines and working with Data Lake
• Understanding of distributed processing, workflow orchestration and performance tuning
• Working knowledge of the banking domain, including banking data and metrics
  • Understanding of data-quality requirements, including validation, completeness, accuracy and consistency