Project description
We are looking for a Data Engineer to design, develop, and maintain robust data pipelines for Deposits and Treasury applications, working with large-scale datasets and enabling financial reporting and analytics use cases.
Responsibilities
- Design, develop, and maintain robust data pipelines for Deposits and Treasury applications.
- Work with large-scale structured and unstructured datasets using Apache Spark / PySpark.
- Develop high-quality, reusable, and efficient code in Python.
- Collaborate with business stakeholders to understand data requirements related to treasury products, liquidity, and deposits.
- Build and optimize ETL/ELT processes for data ingestion, transformation, and integration.
- Support data modelling for financial reporting and analytics use cases.
- Create and maintain data visualizations and dashboards using tools such as Amazon Quicksight, Power BI, Tableau, etc.
- Ensure data quality, governance, and compliance with financial regulations.
- Troubleshoot performance issues and optimize data workflows.
- Work closely with cross-functional teams including analysts, architects, and product owners.
Skills Must have
- At least 6 years of experience in Data Engineering space.
- Strong experience building and maintaining data pipelines for banking data domains.
- Hands on expertise with Apache Spark / PySpark for large-scale data processing.
- Strong Python development skills with emphasis on reusable, efficient code.
- Solid ETL/ELT engineering experience (ingestion, transformation, integration).
- Experience supporting data modelling for reporting/analytics use cases.
- Exposure to BI/dashboarding tools such as Amazon Quicksight, Power BI, Tableau (or similar).
- Practical experience with data quality, governance, and working in regulated environments.
Nice to have
N/A