Description

Key Responsibilities:

• Architect and implement scalable data pipelines and ETL workflows using ADF, PySpark, and DBT.
• Design and optimize data models for Snowflake and other cloud-based data platforms.
• Develop and maintain complex data transformation logic using Python, PL/SQL, DBT, Snowpark, Snowsight web interface, Databricks notebooks, and Pyspark.
• Integrate job schedulers and Azure Function Apps for orchestration and automation.
• Apply Gen AI tools (Cortex AI, GitHub Copilot) to accelerate development, improve code quality, and enhance productivity.
• Ensure robust data governance, security, and compliance across all solutions.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
• Mentor junior engineers and provide technical leadership in data engineering best practices.
• Participate in Scrum ceremonies and contribute to continuous improvement initiatives.

Requirements & Qualifications:

• 6+ years of hands-on experience in data engineering with strong coding skills.
• Proven expertise in: dbt, Snowflake, Azure Data Factory (ADF), Python, PySpark, PL/SQL, Databricks.
• Experience with job schedulers and Azure Function Apps for automation.
• Familiarity with Gen AI tools (e.g., Cortex AI, GitHub Copilot) for accelerating development.
• Strong hands-on understanding of CI/CD pipelines, DevSecOps, Data Engineering best practices, and Agile methodologies.
• Excellent problem-solving skills and ability to work in a fast-paced environment.
• Strong communication and stakeholder management skills.

Education & Certifications:

• Bachelor of Engineering degree in Computer Science, Information Technology, or related field.
• Certifications in Azure Data Engineering, Snowflake, or Databricks are a plus.
• Exposure to Investment Management or Financial Services domain preferred

Key Skills

Education

Bachelor's degree