We are seeking an experienced Senior Data Engineer to join our team and support a key client in the banking sector. The ideal candidate will have a strong background in risk data modeling, SQL, Python, and data warehousing while utilizing cloud platforms and orchestration tools to enhance data pipelines and analytics.
Key Responsibilities:
- Design, develop, and maintain scalable data pipelines to process risk-related data, including PD, EAD, LGD, DOD, and RWA.
- Work with large structured and unstructured datasets, ensuring data integrity, security, and governance.
- Develop efficient SQL queries and Python scripts for ETL processes, optimizing data extraction, transformation, and loading.
- Implement data warehousing best practices to ensure scalable and efficient data storage solutions.
- Manage and optimize AS400 & DB2 databases for banking-related applications.
- Utilize Azure cloud technologies, including Databricks, for data processing, analytics, and machine learning applications.
- Develop and maintain data orchestration pipelines using Apache Airflow and Azure Data Factory.
- Collaborate with business analysts, risk teams, and other stakeholders to understand data requirements and deliver high-quality solutions.
- Ensure compliance with banking regulations and data governance standards.
Required Skills & Qualifications:
- 5+ years of experience in data engineering, data management, or a related role.
- Strong expertise in SQL and Python for data processing and analysis.
- In-depth understanding of risk data models and credit risk landscapes (PD, EAD, LGD, DOD, RWA, etc.).
- Solid experience with data warehousing principles and database management.
- Hands-on experience working with AS400 & DB2 databases.
- Proficiency in Azure cloud technologies, particularly Databricks.
- Experience with data orchestration tools such as Apache Airflow and Azure Data Factory.
- Excellent problem-solving and analytical skills.
- Ability to thrive in a fast-paced banking environment and collaborate effectively with cross-functional teams.
Preferred Qualifications:
- Experience working in the banking or financial sector.
- Familiarity with big data frameworks and machine learning models for risk analytics.
- Understanding of regulatory and compliance requirements in banking.