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
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