Key Responsibilities:
- Design, develop, and maintain scalable and efficient data pipelines using Snowflake, PySpark, and SQL.
- Write optimized and complex SQL queries to extract, transform, and load data.
- Develop and implement data models, schemas, and architecture that support banking domain requirements.
- Collaborate with data analysts, data scientists, and business stakeholders to gather data requirements.
- Automate data workflows and ensure data quality, accuracy, and integrity.
- Manage and coordinate release processes for data pipelines and analytics solutions.
- Monitor, troubleshoot, and optimize the performance of data systems.
- Ensure compliance with data governance, security, and privacy standards within the banking domain.
- Maintain documentation of data architecture, pipelines, and processes.
- Stay updated with the latest industry trends and incorporate best practices.
Required Skills and Experience:
- Proven experience as a Data Engineer or in a similar role with a focus on Snowflake, Python, PySpark, and SQL.
- Strong understanding of data warehousing concepts and cloud data platforms, especially Snowflake.
- Hands-on experience with release management, deployment, and version control practices.
- Solid understanding of banking and financial services industry data and compliance requirements.
- Proficiency in Python scripting and Pyspark for data processing and automation.
- Experience with ETL/ELT processes and tools.
- Knowledge of data governance, security, and privacy standards.
- Excellent problem-solving and analytical skills.
- Strong communication and collaboration abilities.
Preferred Qualifications:
- Good Knowledge in Azure and Databricks in highly preferred.
- Knowledge of Apache Kafka or other streaming technologies.
- Familiarity with DevOps practices and CI/CD pipelines.
- Prior experience working in the banking or financial services industry.
