Data Engineer

Green Key Resources; New York (NY); On-site; USD 110,000 - 160,000; Full time

Job summary

Green Key Resources is seeking a Data Engineer to design, build, and optimize data pipelines and analytics infrastructure supporting financial decision-making. You will collaborate with a dynamic analytics team to deliver reliable data for lending decisions and reporting.

This role centers on Snowflake, dbt, Dagster, and AWS, with responsibilities for ingestion, storage, governance, and auditability. You will document data structures, implement robust ETL/ELT patterns, and partner with

Qualifications

• Bachelor's degree in a data-related field.
• Minimum of 2 years in Data/Analytics Engineering roles.
• Proficiency in SQL and Python for data pipelines (Pandas).
• Hands-on with Snowflake, dbt, Dagster and AWS services.
• Strong data modeling, ETL/ELT design, and governance knowledge.
• Experience with finance-related data and REST API integration preferred.
• Good communication with technical and non-technical stakeholders.

Responsibilities

• Design and optimize batch data pipelines and ELT processes for efficient data ingestion and retrieval.
• Develop and extend dbt projects, focusing on schema design and pipeline reliability.
• Maintain Dagster orchestration for batch ingestion pipelines, ensuring reliable scheduling and observability.
• Build and manage Snowflake-AWS integrations, including S3 storage and IAM roles.
• Support data quality workflows, identifying inconsistencies and implementing resolutions.
• Define and maintain data models and schemas for efficient analytics and reporting.
• Document data structures and pipeline flows for team collaboration and audit purposes.
• Collaborate with stakeholders to translate business requirements into reliable data infrastructure.

Skills

SQL

Python

Pandas

Data modeling

ETL/ELT design

Governance principles

Communication

Education

Bachelor's degree in Data Science, Information Systems, Computer Science, or related field

Tools

Snowflake

dbt

Dagster

AWS

Job description

• Collaborate with a dynamic data team to design, build, and optimize data pipelines and infrastructure supporting financial analytics.
• Work primarily with Snowflake, dbt, Dagster, and AWS to manage data ingestion and storage processes.
• Support a team of Analytics Engineers by ensuring accurate and reliable data for lending decisions.
• Engage with external counterparties to address data-related matters and maintain quality standards.
• Contribute to AI agent operations, supporting deployment and maintenance of AI-driven data workflows.
• Participate in a fast-paced environment, adapting to evolving business requirements and emerging tools.
• Ensure data governance principles are upheld in all data modeling and transformation tasks.
• Document data processes comprehensively to support team knowledge-sharing and auditability.

Key Responsibilities & Duties

• Design and optimize batch data pipelines and ELT processes for efficient data ingestion and retrieval.
• Develop and extend dbt projects, focusing on schema design and pipeline reliability.
• Maintain Dagster orchestration for batch ingestion pipelines, ensuring reliable scheduling and observability.
• Build and manage Snowflake-AWS integrations, including S3 storage and IAM roles.
• Support data quality workflows, identifying inconsistencies and implementing resolutions.
• Define and maintain data models and schemas for efficient analytics and reporting.
• Document data structures and pipeline flows for team collaboration and audit purposes.
• Collaborate with stakeholders to translate business requirements into reliable data infrastructure.

Job Requirements

• Bachelor's degree in Data Science, Information Systems, Computer Science, or related field.
• Minimum of 2 years of experience in Data Engineering or Analytics Engineering roles.
• Proficiency in SQL and Python for data pipeline development, including Pandas.
• Hands-on experience with Snowflake, dbt, Dagster, and AWS services.
• Solid understanding of data modeling, ETL/ELT design patterns, and governance principles.
• Experience with finance-related data and REST API integration preferred.
• Familiarity with AI/ML tooling or LLM-based pipeline development is a plus.
• Effective communication skills with technical and non-technical stakeholders.