Information Technology - ETL Developer

Introduction

Lead the design, and implementation of the enterprise data ecosystem, driving the modernization and consolidation of data platforms to support business intelligence, advanced analytics, and regulatory reporting. This includes a critical focus on the design, development, and maintenance of data warehousing solutions that support credit risk modeling.

Responsibilities

• Provide strategic vision for data architecture, platforms, and governance, translating business objectives into effective, scalable, and secure technical solutions.
• Lead the end-to-end design and implementation of a scalable enterprise data ecosystem, migrating legacy systems into a modern, consolidated cloud environment.
• Architect and maintain robust data warehouse structures in Snowflake to support complex credit risk models, ensuring data lineage, accuracy, and auditability for regulatory compliance.
• Build and optimize high-performance ETL/ELT pipelines using Python, ensuring seamless data flow from disparate sources into the central warehouse.
• Design and manage complex workflows using data orchestration tools (e.g., Airflow) to ensure high availability and reliability of data feeds.
• Drive the transition from fragmented data silos to a unified platform, implementing best practices in data modeling.

Requirements

Required Skills

• 8+ years in Data Engineering
• Deep experience in Snowflake architecture
• Expert-level knowledge of SQL and dimensional data modeling techniques
• Mastery of Python for data manipulation, automation, and API integrations
• Extensive experience with enterprise-grade orchestration tools (e.g., Apache Airflow)

Preferred Skills

• Datastage ETL experience
• CI/CD pipeline deployment processes

Daily Tasks and Responsibilities

• Lead technical workshops to capture business and technical requirements, particularly those related to data warehousing solutions that support credit risk modeling.
• Coding, testing, and deploying processes to extract, transform, and load data from various sources.
• Validating data to ensure accuracy and consistency, which is crucial for reliable analysis.
• Perform technical evaluations of new data technologies and features, building proof-of-concepts to validate architectural approaches.
• Serve as the key technical liaison between the engineering teams.