Data Engineer

Data Engineer Overview

• The Data Engineer will design and implement scalable data infrastructure within Snowflake to support advanced analytics and decision-making processes.
• Collaborate with cross-functional teams to deliver high-quality, governed, and reliable data systems for strategic insights.
• Work closely with investment professionals and stakeholders to translate business needs into technical solutions.
• Develop and optimize data models and pipelines for efficient and cost-effective data processing.
• Lead data governance initiatives, ensuring adherence to standards and practices for data quality and security.
• Contribute to the development of artificial intelligence enablement and advanced analytics capabilities.
• Enhance Snowflake performance through warehouse sizing, query tuning, and workload management.
• Support the organization’s hybrid working policy with opportunities for professional growth and development.

Data Engineer Key Responsibilities & Duties

• Develop, optimize, and maintain data models and pipelines within Snowflake for structured and unstructured data.
• Build workflows to ingest, transform, and consolidate internal and external data sources.
• Implement data governance frameworks, including ownership, lineage tracking, and access controls.
• Monitor and remediate data quality issues across various datasets and systems.
• Design pipelines to process unstructured content for analytics and artificial intelligence applications.
• Collaborate with finance, operations, and investment teams to align technical solutions with business needs.
• Optimize Snowflake performance, ensuring cost-effective and efficient data operations.
• Stay updated on emerging technologies to enhance productivity and innovation in data engineering.

Data Engineer Job Requirements

• Bachelor of Science degree in a relevant field, such as Computer Science or Engineering.
• Minimum of 3 years of experience in data engineering or analytics roles; 6+ years preferred.
• Proficiency in Snowflake, SQL, and Python for data processing and pipeline development.
• Experience with cloud-based workflows and orchestration systems for scalable data solutions.
• Knowledge of data governance frameworks and enterprise data quality standards.
• Familiarity with artificial intelligence tools and technologies is advantageous.
• Strong analytical skills and ability to translate business requirements into technical implementations.
• Commitment to professional development and continuous learning in data engineering practices.