Data Engineer

Overview

• Design, develop, and maintain scalable data infrastructure and analytics solutions for data-driven decision-making.
• Collaborate with internal stakeholders and external counterparties to address diverse data-related needs.
• Work with large, complex datasets to build robust data pipelines ensuring data accuracy and integrity.
• Support efficient data collection, storage, and retrieval through optimized ETL/ELT processes.
• Enable efficient data analysis, reporting, and visualization through data modeling and schema design.
• Implement data governance and quality standards to ensure reliable analytics solutions.
• Document data processes and communicate technical concepts effectively to non-technical stakeholders.
• Stay current on emerging tools, technologies, and best practices in data engineering.

Key Responsibilities & Duties

• Develop and optimize data pipelines, ETL/ELT processes, and data warehouses for scalable data solutions.
• Define data models and schemas to support analysis, reporting, and visualization needs.
• Cleanse, transform, and aggregate data to derive actionable insights and address inconsistencies.
• Collaborate with departments to understand requirements and develop tailored analytics solutions.
• Manage external counterparties for reconciliations, data issues, and additional data requests.
• Document data flows and structures, ensuring clarity for technical and non-technical teams.
• Implement data governance practices to maintain high-quality and reliable data outputs.
• Utilize tools like DBT, Snowflake, AWS, and Python for data transformation and analysis.

Job Requirements

• Bachelor's degree in Computer Science, Information Systems, Data Science, or related field.
• Minimum 5 years of experience in data engineering or analytics roles; 7 years preferred.
• Proficiency in SQL, DBT, Snowflake, AWS, and Python for data manipulation and analysis.
• Strong understanding of data modeling, governance, ETL/ELT practices, and orchestration tools.
• Experience with API usage and integration for data workflows.
• Excellent analytical and problem-solving skills for deriving insights from complex datasets.
• Effective communication skills for collaboration across technical and non-technical teams.
• Commitment to high-quality output and meticulous attention to detail.