Data Engineer

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.